{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "464b32d5-56c1-4ada-a8ad-7704129dff91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ipynb is to explore the use of VTD data for HD code\n",
      "I read in the Census tract data\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>VTDST20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>VTDI20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>LSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08</td>\n",
       "      <td>017</td>\n",
       "      <td>017001</td>\n",
       "      <td>08017017001</td>\n",
       "      <td>A</td>\n",
       "      <td>Cheyenne 001</td>\n",
       "      <td>Cheyenne 001</td>\n",
       "      <td>00</td>\n",
       "      <td>G5240</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.27912 39.04635, -102.27756 39.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08</td>\n",
       "      <td>017</td>\n",
       "      <td>017002</td>\n",
       "      <td>08017017002</td>\n",
       "      <td>A</td>\n",
       "      <td>Cheyenne 002</td>\n",
       "      <td>Cheyenne 002</td>\n",
       "      <td>00</td>\n",
       "      <td>G5240</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.35550 38.81684, -102.35549 38.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08</td>\n",
       "      <td>017</td>\n",
       "      <td>017003</td>\n",
       "      <td>08017017003</td>\n",
       "      <td>A</td>\n",
       "      <td>Cheyenne 003</td>\n",
       "      <td>Cheyenne 003</td>\n",
       "      <td>00</td>\n",
       "      <td>G5240</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.62095 38.69297, -102.61838 38.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08</td>\n",
       "      <td>017</td>\n",
       "      <td>017004</td>\n",
       "      <td>08017017004</td>\n",
       "      <td>A</td>\n",
       "      <td>Cheyenne 004</td>\n",
       "      <td>Cheyenne 004</td>\n",
       "      <td>00</td>\n",
       "      <td>G5240</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-103.01602 38.77913, -103.01599 38.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08</td>\n",
       "      <td>017</td>\n",
       "      <td>017005</td>\n",
       "      <td>08017017005</td>\n",
       "      <td>A</td>\n",
       "      <td>Cheyenne 005</td>\n",
       "      <td>Cheyenne 005</td>\n",
       "      <td>00</td>\n",
       "      <td>G5240</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-103.17270 38.62502, -103.17262 38.6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 348 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 VTDST20      GEOID20 VTDI20        NAME20  \\\n",
       "0        08        017  017001  08017017001      A  Cheyenne 001   \n",
       "1        08        017  017002  08017017002      A  Cheyenne 002   \n",
       "2        08        017  017003  08017017003      A  Cheyenne 003   \n",
       "3        08        017  017004  08017017004      A  Cheyenne 004   \n",
       "4        08        017  017005  08017017005      A  Cheyenne 005   \n",
       "\n",
       "     NAMELSAD20 LSAD20 MTFCC20 FUNCSTAT20  ...  P0050002  P0050003 P0050004  \\\n",
       "0  Cheyenne 001     00   G5240          N  ...         0         0        0   \n",
       "1  Cheyenne 002     00   G5240          N  ...         0         0        0   \n",
       "2  Cheyenne 003     00   G5240          N  ...        27         0        0   \n",
       "3  Cheyenne 004     00   G5240          N  ...         0         0        0   \n",
       "4  Cheyenne 005     00   G5240          N  ...         0         0        0   \n",
       "\n",
       "  P0050005 P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0        0   \n",
       "1        0        0        0        0        0        0   \n",
       "2       27        0        0        0        0        0   \n",
       "3        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-102.27912 39.04635, -102.27756 39.0...  \n",
       "1  POLYGON ((-102.35550 38.81684, -102.35549 38.8...  \n",
       "2  POLYGON ((-102.62095 38.69297, -102.61838 38.6...  \n",
       "3  POLYGON ((-103.01602 38.77913, -103.01599 38.7...  \n",
       "4  POLYGON ((-103.17270 38.62502, -103.17262 38.6...  \n",
       "\n",
       "[5 rows x 348 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"This ipynb is to explore the use of VTD data for HD code\") #copied 9/12/22 from original OH code\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/co_pl2020_vtd.dbf\") #**CHANGE FILENAME AS NEEDED*****\n",
    "tractPopFile.head() \n",
    "print(\"I read in the Census tract data\")\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)         \n",
    "def plotCenter(t,geom):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae933c8a-74cf-491c-9e01-f4d11e14bbe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 3108 vtd tracts for CO using Census vtds\n",
      "CO has 63 counties and a total population of 5773714\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "STATE = \"CO\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractNAME20 = tractPopFile['NAME20']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #USE VAP.  Can use ACS CVAP if more accurate data needed\n",
    "tractHisp = tractPopFile['P0040002']   #USE VAP.  Can use ACS CVAP if more accurate data needed\n",
    "tractBlack = tractPopFile['P0030004']  #USE VAP.  Can use ACS CVAP if more accurate data needed\n",
    "tractCountyNo = tractPopFile['COUNTYFP20']\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} vtd tracts for {1} using {2}\".format(nTracts, STATE,\"Census vtds\") )\n",
    "tractArea = [0.]*nTracts\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractCP = [Point(0,0)]*nTracts\n",
    "tractCountyNo = tractCountyNo.to_numpy()\n",
    "countyNo = [0]*nTracts  #the tract's county number\n",
    "tractNo = [0]*nTracts  #original row number in tractPopFile\n",
    "for t in range (0,nTracts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    countyNo[t] = int((int(tractCountyNo[t]) - 1)/2)\n",
    "    tractCP[t] = Point(tractGeom[t].centroid)\n",
    "    tractCPx[t] = tractCP[t].x\n",
    "    tractCPy[t] = tractCP[t].y\n",
    "    tractNo[t] = t\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)\n",
    "nCounties = int( np.max(countyNo)+1)\n",
    "statePop = np.sum(tractPop)\n",
    "print(STATE,\"has\",nCounties,\"counties and a total population of\",statePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6344566b-282a-4b8e-a174-4369489c9274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now read in the voting data from a separate file\n",
      "I have read in the 2020 voting data for all 3215 precincts from 63 counties\n",
      "There are 3168959 voters vs 5773714 inhabitants in 3108 census VTDs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>VTDST</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PREUWES</th>\n",
       "      <th>G20PREOOTH</th>\n",
       "      <th>G20USSDHIC</th>\n",
       "      <th>G20USSRGAR</th>\n",
       "      <th>G20USSLDOA</th>\n",
       "      <th>G20USSODOY</th>\n",
       "      <th>G20USSOEVA</th>\n",
       "      <th>G20USSOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>001226</td>\n",
       "      <td>Adams 226</td>\n",
       "      <td>6253001226</td>\n",
       "      <td>60</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>131</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.67850 39.97236 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>001092</td>\n",
       "      <td>Adams 092</td>\n",
       "      <td>7243401092</td>\n",
       "      <td>653</td>\n",
       "      <td>579</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>649</td>\n",
       "      <td>572</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.96793 39.91400 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>001089</td>\n",
       "      <td>Adams 089</td>\n",
       "      <td>7243101089</td>\n",
       "      <td>666</td>\n",
       "      <td>454</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>645</td>\n",
       "      <td>469</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.95210 39.92118 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>001087</td>\n",
       "      <td>Adams 087</td>\n",
       "      <td>7243401087</td>\n",
       "      <td>690</td>\n",
       "      <td>467</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>668</td>\n",
       "      <td>475</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-104.95909 39.92851 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>001088</td>\n",
       "      <td>Adams 088</td>\n",
       "      <td>7243101088</td>\n",
       "      <td>706</td>\n",
       "      <td>516</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>695</td>\n",
       "      <td>543</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.94884 39.92847 0.00000, -104....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP   VTDST       NAME    PRECINCT  G20PREDBID  G20PRERTRU  \\\n",
       "0      08        0  001226  Adams 226  6253001226          60         128   \n",
       "1      08        0  001092  Adams 092  7243401092         653         579   \n",
       "2      08        0  001089  Adams 089  7243101089         666         454   \n",
       "3      08        0  001087  Adams 087  7243401087         690         467   \n",
       "4      08        0  001088  Adams 088  7243101088         706         516   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PREUWES  G20PREOOTH  G20USSDHIC  \\\n",
       "0           1           1           0           0           0          60   \n",
       "1          22           3           2           1           6         649   \n",
       "2          26           5           1           2           7         645   \n",
       "3          31           5           4           6           6         668   \n",
       "4          21           1           3           4           5         695   \n",
       "\n",
       "   G20USSRGAR  G20USSLDOA  G20USSODOY  G20USSOEVA  G20USSOWRI  \\\n",
       "0         131           3           0           1           0   \n",
       "1         572          37           4           1           0   \n",
       "2         469          37           6           2           0   \n",
       "3         475          50           5           7           1   \n",
       "4         543          12           7           5           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((-104.67850 39.97236 0.00000, -104....  \n",
       "1  POLYGON Z ((-104.96793 39.91400 0.00000, -104....  \n",
       "2  POLYGON Z ((-104.95210 39.92118 0.00000, -104....  \n",
       "3  POLYGON Z ((-104.95909 39.92851 0.00000, -104....  \n",
       "4  POLYGON Z ((-104.94884 39.92847 0.00000, -104....  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"I will now read in the voting data from a separate file\")\n",
    "vestFile = gpd.read_file(\"state_map_files/co_vest_20.dbf\")\n",
    "vestFile = vestFile.to_crs(tractPopFile.crs) #many states have precinct data in alt CRS\n",
    "vestGeom = vestFile['geometry']\n",
    "vestVTDST = vestFile['VTDST']\n",
    "vestTrump = vestFile['G20PRERTRU']\n",
    "vestBiden = vestFile['G20PREDBID']\n",
    "vestGEOID20 = vestFile['PRECINCT']  #'GEOID20'\n",
    "vestTrump = vestTrump.to_numpy()\n",
    "vestBiden = vestBiden.to_numpy()\n",
    "vestNAME = vestFile[\"NAME\"] #official full name of precinct  #'NAME20' for some other states\n",
    "nPrecincts = len(vestNAME)\n",
    "vestCountyNo = vestFile['COUNTYFP']  #'COUNTYFP20'\n",
    "vestCountyNo = vestCountyNo.to_numpy()\n",
    "vestCPx = [0.]*nPrecincts  #these two are the x- and y-coordinates of the centroid of each vtd shape\n",
    "vestCPy = [0.]*nPrecincts\n",
    "vestArea = [0.]*nPrecincts\n",
    "vestPrecinctNo = [0]*nPrecincts\n",
    "for p in range (0,nPrecincts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    vestCountyNo[p] = int((int(vestCountyNo[p]) - 1)/2)  #int(vestCountyNo[p])    #this operation not already done by VEST\n",
    "    vestCPx[p] = vestGeom[p].centroid.x\n",
    "    vestCPy[p] = vestGeom[p].centroid.y\n",
    "    vestArea[p]= vestGeom[p].area\n",
    "    vestPrecinctNo[p] = p  #in case we re-sort\n",
    "print(\"I have read in the 2020 voting data for all\",nPrecincts,\"precincts from\",np.max(vestCountyNo)+1,\"counties\")\n",
    "print(\"There are\",np.sum(vestTrump)+np.sum(vestBiden),\"voters vs\",np.sum(tractPop),\"inhabitants in\",nTracts,\"census VTDs\")\n",
    "vestFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c50ab92-8884-4946-9ac7-1c1d4d702ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>VTDST</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PREUWES</th>\n",
       "      <th>G20PREOOTH</th>\n",
       "      <th>G20USSDHIC</th>\n",
       "      <th>G20USSRGAR</th>\n",
       "      <th>G20USSLDOA</th>\n",
       "      <th>G20USSODOY</th>\n",
       "      <th>G20USSOEVA</th>\n",
       "      <th>G20USSOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3210</th>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>001003</td>\n",
       "      <td>Adams 003</td>\n",
       "      <td>7213201003</td>\n",
       "      <td>646</td>\n",
       "      <td>222</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>622</td>\n",
       "      <td>227</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.98865 39.79111 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>08</td>\n",
       "      <td>43</td>\n",
       "      <td>087001</td>\n",
       "      <td>Morgan 001</td>\n",
       "      <td>4016544001</td>\n",
       "      <td>166</td>\n",
       "      <td>773</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>163</td>\n",
       "      <td>777</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.15006 40.21797 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3212</th>\n",
       "      <td>08</td>\n",
       "      <td>43</td>\n",
       "      <td>087018</td>\n",
       "      <td>Morgan 018</td>\n",
       "      <td>4016544018</td>\n",
       "      <td>168</td>\n",
       "      <td>735</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>740</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.15033 40.00543 0.00000, -104....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3213</th>\n",
       "      <td>08</td>\n",
       "      <td>43</td>\n",
       "      <td>087004</td>\n",
       "      <td>Morgan 004</td>\n",
       "      <td>4016544004</td>\n",
       "      <td>170</td>\n",
       "      <td>687</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>155</td>\n",
       "      <td>696</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-103.98101 40.16024 0.00000, -103....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>08</td>\n",
       "      <td>61</td>\n",
       "      <td>123312</td>\n",
       "      <td>Weld 312</td>\n",
       "      <td>4016362312</td>\n",
       "      <td>85</td>\n",
       "      <td>423</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>426</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-104.14998 40.24998 0.00000, -104....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     STATEFP COUNTYFP   VTDST        NAME    PRECINCT  G20PREDBID  G20PRERTRU  \\\n",
       "3210      08        0  001003   Adams 003  7213201003         646         222   \n",
       "3211      08       43  087001  Morgan 001  4016544001         166         773   \n",
       "3212      08       43  087018  Morgan 018  4016544018         168         735   \n",
       "3213      08       43  087004  Morgan 004  4016544004         170         687   \n",
       "3214      08       61  123312    Weld 312  4016362312          85         423   \n",
       "\n",
       "      G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PREUWES  G20PREOOTH  G20USSDHIC  \\\n",
       "3210          11           3           0           4           4         622   \n",
       "3211          16           0           2           4           2         163   \n",
       "3212          18           2           1           1           4         164   \n",
       "3213          11           0           0           1           3         155   \n",
       "3214           7           1           1           0           2          84   \n",
       "\n",
       "      G20USSRGAR  G20USSLDOA  G20USSODOY  G20USSOEVA  G20USSOWRI  \\\n",
       "3210         227          20           3           3           0   \n",
       "3211         777          11           3           1           0   \n",
       "3212         740          13           6           2           0   \n",
       "3213         696           4           2           1           0   \n",
       "3214         426           4           0           2           0   \n",
       "\n",
       "                                               geometry  \n",
       "3210  POLYGON Z ((-104.98865 39.79111 0.00000, -104....  \n",
       "3211  POLYGON Z ((-104.15006 40.21797 0.00000, -104....  \n",
       "3212  POLYGON Z ((-104.15033 40.00543 0.00000, -104....  \n",
       "3213  POLYGON Z ((-103.98101 40.16024 0.00000, -103....  \n",
       "3214  POLYGON Z ((-104.14998 40.24998 0.00000, -104....  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vestFile.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49759ce1-d130-4ccf-b6a9-48d2563c3112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building a list of names of tracts for quick lookup\n"
     ]
    }
   ],
   "source": [
    "print(\"building a list of names of tracts for quick lookup\")\n",
    "tractNAME20List = []\n",
    "for t in range(nTracts):\n",
    "    tractNAME20List.append(tractNAME20[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5d986a16-356b-4d78-83ec-7ba800d48bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on precinct 0\n",
      "working on precinct 300\n",
      "working on precinct 600\n",
      "unmatched precinct 848 with voter pop = 799\n",
      "working on precinct 900\n",
      "unmatched precinct 926 with voter pop = 1662\n",
      "unmatched precinct 1033 with voter pop = 1800\n",
      "unmatched precinct 1068 with voter pop = 930\n",
      "unmatched precinct 1099 with voter pop = 1974\n",
      "unmatched precinct 1100 with voter pop = 1010\n",
      "unmatched precinct 1101 with voter pop = 1081\n",
      "unmatched precinct 1102 with voter pop = 702\n",
      "unmatched precinct 1139 with voter pop = 1659\n",
      "unmatched precinct 1141 with voter pop = 2154\n",
      "unmatched precinct 1180 with voter pop = 943\n",
      "working on precinct 1200\n",
      "unmatched precinct 1251 with voter pop = 939\n",
      "unmatched precinct 1277 with voter pop = 983\n",
      "unmatched precinct 1278 with voter pop = 874\n",
      "unmatched precinct 1281 with voter pop = 1523\n",
      "unmatched precinct 1289 with voter pop = 1511\n",
      "unmatched precinct 1294 with voter pop = 1230\n",
      "unmatched precinct 1316 with voter pop = 1318\n",
      "unmatched precinct 1330 with voter pop = 1205\n",
      "unmatched precinct 1350 with voter pop = 652\n",
      "unmatched precinct 1357 with voter pop = 954\n",
      "unmatched precinct 1361 with voter pop = 1432\n",
      "unmatched precinct 1365 with voter pop = 990\n",
      "unmatched precinct 1374 with voter pop = 854\n",
      "working on precinct 1500\n",
      "working on precinct 1800\n",
      "unmatched precinct 2073 with voter pop = 994\n",
      "unmatched precinct 2074 with voter pop = 1307\n",
      "unmatched precinct 2075 with voter pop = 1403\n",
      "unmatched precinct 2076 with voter pop = 1508\n",
      "unmatched precinct 2077 with voter pop = 1530\n",
      "unmatched precinct 2078 with voter pop = 1567\n",
      "unmatched precinct 2079 with voter pop = 728\n",
      "unmatched precinct 2080 with voter pop = 1243\n",
      "unmatched precinct 2081 with voter pop = 1085\n",
      "unmatched precinct 2082 with voter pop = 1300\n",
      "unmatched precinct 2083 with voter pop = 985\n",
      "unmatched precinct 2084 with voter pop = 917\n",
      "unmatched precinct 2085 with voter pop = 1413\n",
      "unmatched precinct 2098 with voter pop = 608\n",
      "working on precinct 2100\n",
      "unmatched precinct 2157 with voter pop = 744\n",
      "unmatched precinct 2303 with voter pop = 1348\n",
      "unmatched precinct 2304 with voter pop = 1327\n",
      "unmatched precinct 2305 with voter pop = 692\n",
      "unmatched precinct 2306 with voter pop = 854\n",
      "unmatched precinct 2307 with voter pop = 1130\n",
      "unmatched precinct 2308 with voter pop = 806\n",
      "unmatched precinct 2309 with voter pop = 673\n",
      "unmatched precinct 2310 with voter pop = 325\n",
      "unmatched precinct 2311 with voter pop = 1027\n",
      "unmatched precinct 2312 with voter pop = 802\n",
      "unmatched precinct 2313 with voter pop = 1060\n",
      "unmatched precinct 2314 with voter pop = 1015\n",
      "unmatched precinct 2380 with voter pop = 1506\n",
      "unmatched precinct 2381 with voter pop = 1031\n",
      "unmatched precinct 2382 with voter pop = 1007\n",
      "unmatched precinct 2383 with voter pop = 615\n",
      "unmatched precinct 2384 with voter pop = 839\n",
      "unmatched precinct 2385 with voter pop = 488\n",
      "unmatched precinct 2386 with voter pop = 1094\n",
      "unmatched precinct 2387 with voter pop = 1237\n",
      "unmatched precinct 2388 with voter pop = 717\n",
      "unmatched precinct 2389 with voter pop = 897\n",
      "working on precinct 2400\n",
      "unmatched precinct 2558 with voter pop = 692\n",
      "unmatched precinct 2570 with voter pop = 1155\n",
      "unmatched precinct 2573 with voter pop = 1159\n",
      "unmatched precinct 2575 with voter pop = 879\n",
      "unmatched precinct 2579 with voter pop = 1115\n",
      "unmatched precinct 2585 with voter pop = 768\n",
      "unmatched precinct 2587 with voter pop = 588\n",
      "unmatched precinct 2588 with voter pop = 749\n",
      "unmatched precinct 2589 with voter pop = 766\n",
      "unmatched precinct 2592 with voter pop = 1144\n",
      "unmatched precinct 2594 with voter pop = 988\n",
      "unmatched precinct 2596 with voter pop = 707\n",
      "unmatched precinct 2598 with voter pop = 1320\n",
      "unmatched precinct 2599 with voter pop = 629\n",
      "working on precinct 2700\n",
      "unmatched precinct 2747 with voter pop = 1946\n",
      "unmatched precinct 2779 with voter pop = 1077\n",
      "unmatched precinct 2780 with voter pop = 807\n",
      "unmatched precinct 2783 with voter pop = 781\n",
      "unmatched precinct 2786 with voter pop = 864\n",
      "unmatched precinct 2787 with voter pop = 1777\n",
      "unmatched precinct 2789 with voter pop = 1086\n",
      "unmatched precinct 2805 with voter pop = 992\n",
      "unmatched precinct 2812 with voter pop = 854\n",
      "unmatched precinct 2813 with voter pop = 1494\n",
      "unmatched precinct 2815 with voter pop = 1235\n",
      "unmatched precinct 2819 with voter pop = 773\n",
      "unmatched precinct 2922 with voter pop = 647\n",
      "unmatched precinct 2940 with voter pop = 1267\n",
      "unmatched precinct 2945 with voter pop = 539\n",
      "unmatched precinct 2946 with voter pop = 854\n",
      "unmatched precinct 2951 with voter pop = 1193\n",
      "unmatched precinct 2952 with voter pop = 736\n",
      "unmatched precinct 2999 with voter pop = 1225\n",
      "working on precinct 3000\n",
      "unmatched precinct 3004 with voter pop = 1347\n",
      "unmatched precinct 3005 with voter pop = 1718\n",
      "unmatched precinct 3006 with voter pop = 1377\n",
      "unmatched precinct 3013 with voter pop = 762\n",
      "unmatched precinct 3017 with voter pop = 737\n",
      "unmatched precinct 3018 with voter pop = 1047\n",
      "unmatched precinct 3019 with voter pop = 900\n",
      "unmatched precinct 3020 with voter pop = 876\n",
      "unmatched precinct 3034 with voter pop = 708\n",
      "unmatched precinct 3035 with voter pop = 806\n",
      "unmatched precinct 3036 with voter pop = 483\n",
      "unmatched precinct 3037 with voter pop = 465\n",
      "unmatched precinct 3040 with voter pop = 776\n",
      "unmatched precinct 3041 with voter pop = 926\n",
      "unmatched precinct 3044 with voter pop = 998\n",
      "unmatched precinct 3045 with voter pop = 752\n",
      "unmatched precinct 3046 with voter pop = 629\n",
      "unmatched precinct 3056 with voter pop = 644\n",
      "unmatched precinct 3061 with voter pop = 847\n",
      "unmatched precinct 3062 with voter pop = 959\n",
      "unmatched precinct 3063 with voter pop = 630\n",
      "unmatched precinct 3073 with voter pop = 878\n",
      "unmatched precinct 3074 with voter pop = 882\n",
      "unmatched precinct 3075 with voter pop = 757\n",
      "unmatched precinct 3080 with voter pop = 672\n",
      "unmatched precinct 3081 with voter pop = 1097\n",
      "unmatched precinct 3082 with voter pop = 661\n",
      "unmatched precinct 3084 with voter pop = 1101\n",
      "unmatched precinct 3085 with voter pop = 752\n",
      "unmatched precinct 3086 with voter pop = 770\n",
      "unmatched precinct 3087 with voter pop = 715\n",
      "unmatched precinct 3112 with voter pop = 762\n",
      "unmatched precinct 3113 with voter pop = 1069\n",
      "unmatched precinct 3147 with voter pop = 755\n",
      "unmatched precinct 3150 with voter pop = 912\n",
      "unmatched precinct 3153 with voter pop = 973\n",
      "unmatched precinct 3157 with voter pop = 886\n",
      "unmatched precinct 3159 with voter pop = 784\n",
      "unmatched precinct 3160 with voter pop = 922\n",
      "unmatched precinct 3162 with voter pop = 929\n",
      "unmatched precinct 3163 with voter pop = 871\n",
      "unmatched precinct 3166 with voter pop = 503\n",
      "unmatched precinct 3169 with voter pop = 678\n",
      "unmatched precinct 3176 with voter pop = 757\n",
      "unmatched precinct 3201 with voter pop = 1039\n",
      "unmatched precinct 3204 with voter pop = 1156\n",
      "unmatched precinct 3205 with voter pop = 675\n",
      "unmatched precinct 3208 with voter pop = 839\n",
      "unmatched precinct 3209 with voter pop = 1054\n",
      "unmatched precinct 3212 with voter pop = 903\n",
      "It looks like there are 144 precincts that have unmatched names in the census VTD file\n",
      "This compares to an expectation nPrecincts - nTracts = 107\n"
     ]
    }
   ],
   "source": [
    "isNoncensusPrecinct = [0]*nPrecincts\n",
    "nNoncensusPrecincts = 0\n",
    "precinctVTDno = [-999]*nPrecincts\n",
    "noncensusPrecinctList = []\n",
    "for p in range(nPrecincts):\n",
    "    if p%300 == 0:\n",
    "        print(\"working on precinct\",p)\n",
    "    if vestNAME[p] not in tractNAME20List:\n",
    "        isNoncensusPrecinct[p] = 1\n",
    "        print(\"unmatched precinct\",p,\"with voter pop =\",vestTrump[p]+vestBiden[p])\n",
    "    else:\n",
    "        precinctVTDno[p] = tractNAME20List.index(vestNAME[p])      \n",
    "nNoncensusPrecincts = np.sum(isNoncensusPrecinct)\n",
    "print(\"It looks like there are\",nNoncensusPrecincts,\"precincts that have unmatched names in the census VTD file\")\n",
    "print(\"This compares to an expectation nPrecincts - nTracts =\",nPrecincts-nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "59ee4144-9463-4f62-a8dd-8a3325a888c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the histogram of census vtds by county\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here is the histogram of precincts by county\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAARYUlEQVR4nO3db4hdd53H8ffHRKtbFdvtJGST7E6FoKaybWXIVrqINq7NWjF9UojgEqSQJ9mlgoskPhEXAvGJ6IOtEOqfgH/KUHUbLOiGaNldWBqnWrdN09DQZpshMTMqxdUHcRO/+2BO2dt00rnN3HF+OfN+QTjn/O7v3Pv9ZiZ8OOfe+0uqCkmSWvO65S5AkqT5GFCSpCYZUJKkJhlQkqQmGVCSpCatXu4CAG644YYaHx9f7jIkScvg8ccf/2VVjV063kRAjY+PMzU1tdxlSJKWQZL/nm/cW3ySpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCY1sdTRqIzveWRR55/af9eIKpEkLZZXUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmDRVQSd6W5KEkzyQ5nuS9Sa5PcjjJs932uoH5e5OcTHIiyZ1LV74kqa+GvYL6EvCDqnoncDNwHNgDHKmqTcCR7pgkm4EdwE3ANuD+JKtGXbgkqd8WDKgkbwXeB3wFoKp+X1UvAtuBg920g8Dd3f524MGqOl9VzwMngS2jLVuS1HfDXEG9HZgFvpbkZ0keSHItsLaqzgJ02zXd/PXA6YHzp7uxl0myK8lUkqnZ2dlFNSFJ6p9hAmo18B7gy1V1K/A7utt5l5F5xuoVA1UHqmqiqibGxsaGKlaStHIME1DTwHRVPdYdP8RcYJ1Lsg6g284MzN84cP4G4MxoypUkrRQLBlRV/QI4neQd3dBW4GngELCzG9sJPNztHwJ2JLkmyY3AJuDoSKuWJPXe6iHn/QPwzSRvAJ4DPsFcuE0muRd4AbgHoKqOJZlkLsQuALur6uLIK5ck9dpQAVVVTwAT8zy09TLz9wH7rrwsSdJK50oSkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmrV7uAtSm8T2PLPo5Tu2/awSVSFqpvIKSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1aaiASnIqyZNJnkgy1Y1dn+Rwkme77XUD8/cmOZnkRJI7l6p4SVJ/vZYrqA9U1S1VNdEd7wGOVNUm4Eh3TJLNwA7gJmAbcH+SVSOsWZK0AizmFt924GC3fxC4e2D8wao6X1XPAyeBLYt4HUnSCjRsQBXwr0keT7KrG1tbVWcBuu2abnw9cHrg3Olu7GWS7EoylWRqdnb2yqqXJPXWsEsd3V5VZ5KsAQ4neeZV5maesXrFQNUB4ADAxMTEKx6XJK1sQ11BVdWZbjsDfI+5W3bnkqwD6LYz3fRpYOPA6RuAM6MqWJK0MiwYUEmuTfKWl/aBDwFPAYeAnd20ncDD3f4hYEeSa5LcCGwCjo66cElSvw1zi28t8L0kL83/VlX9IMlPgMkk9wIvAPcAVNWxJJPA08AFYHdVXVyS6iVJvbVgQFXVc8DN84z/Cth6mXP2AfsWXZ0kacVyJQlJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKThg6oJKuS/CzJ97vj65McTvJst71uYO7eJCeTnEhy51IULknqt9dyBXUfcHzgeA9wpKo2AUe6Y5JsBnYANwHbgPuTrBpNuZKklWKogEqyAbgLeGBgeDtwsNs/CNw9MP5gVZ2vqueBk8CWkVQrSVoxhr2C+iLwaeAPA2Nrq+osQLdd042vB04PzJvuxl4mya4kU0mmZmdnX2vdkqSeWzCgknwEmKmqx4d8zswzVq8YqDpQVRNVNTE2NjbkU0uSVorVQ8y5Hfhokg8DbwTemuQbwLkk66rqbJJ1wEw3fxrYOHD+BuDMKIuWJPXfgldQVbW3qjZU1ThzH374UVV9HDgE7Oym7QQe7vYPATuSXJPkRmATcHTklUuSem2YK6jL2Q9MJrkXeAG4B6CqjiWZBJ4GLgC7q+rioiuVJK0orymgqupR4NFu/1fA1svM2wfsW2RtkqQVzJUkJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU1azH/5Lr2q8T2PLOr8U/vvGlElkq5GXkFJkppkQEmSmmRASZKa5HtQ0gJ8L01aHl5BSZKaZEBJkppkQEmSmmRASZKaZEBJkpq0YEAleWOSo0l+nuRYks9149cnOZzk2W573cA5e5OcTHIiyZ1L2YAkqZ+GuYI6D9xRVTcDtwDbktwG7AGOVNUm4Eh3TJLNwA7gJmAbcH+SVUtQuySpxxYMqJrz2+7w9d2fArYDB7vxg8Dd3f524MGqOl9VzwMngS2jLFqS1H9DvQeVZFWSJ4AZ4HBVPQasraqzAN12TTd9PXB64PTpbuzS59yVZCrJ1Ozs7CJakCT10VABVVUXq+oWYAOwJcm7X2V65nuKeZ7zQFVNVNXE2NjYUMVKklaO1/Qpvqp6EXiUufeWziVZB9BtZ7pp08DGgdM2AGcWW6gkaWVZcC2+JGPA/1bVi0neBHwQ+DxwCNgJ7O+2D3enHAK+leQLwJ8Bm4CjS1B7kxa7bhu4dpskwXCLxa4DDnafxHsdMFlV30/yn8BkknuBF4B7AKrqWJJJ4GngArC7qi4uTfmSpL5aMKCq6r+AW+cZ/xWw9TLn7AP2Lbo6SdKK5UoSkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYNs5KEpGW22CW0XD5LVyOvoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNWjCgkmxM8uMkx5McS3JfN359ksNJnu221w2cszfJySQnkty5lA1IkvppmCuoC8CnqupdwG3A7iSbgT3AkaraBBzpjuke2wHcBGwD7k+yaimKlyT114IBVVVnq+qn3f7/AMeB9cB24GA37SBwd7e/HXiwqs5X1fPASWDLiOuWJPXca3oPKsk4cCvwGLC2qs7CXIgBa7pp64HTA6dNd2OXPteuJFNJpmZnZ6+gdElSnw0dUEneDHwH+GRV/ebVps4zVq8YqDpQVRNVNTE2NjZsGZKkFWKogEryeubC6ZtV9d1u+FySdd3j64CZbnwa2Dhw+gbgzGjKlSStFMN8ii/AV4DjVfWFgYcOATu7/Z3AwwPjO5Jck+RGYBNwdHQlS5JWgtVDzLkd+DvgySRPdGOfAfYDk0nuBV4A7gGoqmNJJoGnmfsE4O6qujjqwiVJ/bZgQFXVfzD/+0oAWy9zzj5g3yLqkiStcK4kIUlqkgElSWqSASVJatIwH5KQJPXM+J5HFv0cp/bfNYJKLs8rKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKT/KKupBXlaviCquZ4BSVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlq0oIBleSrSWaSPDUwdn2Sw0me7bbXDTy2N8nJJCeS3LlUhUuS+m2YK6ivA9suGdsDHKmqTcCR7pgkm4EdwE3dOfcnWTWyaiVJK8aCAVVV/wb8+pLh7cDBbv8gcPfA+INVdb6qngdOAltGU6okaSW50veg1lbVWYBuu6YbXw+cHpg33Y29QpJdSaaSTM3Ozl5hGZKkvhr1hyQyz1jNN7GqDlTVRFVNjI2NjbgMSdLV7koD6lySdQDddqYbnwY2DszbAJy58vIkSSvVlQbUIWBnt78TeHhgfEeSa5LcCGwCji6uREnSSrR6oQlJvg28H7ghyTTwWWA/MJnkXuAF4B6AqjqWZBJ4GrgA7K6qi0tUuySpxxYMqKr62GUe2nqZ+fuAfYspStJoje95ZNHPcWr/XSOoRBqeK0lIkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkpq04PegpOXid3f6Z7E/U3+eK4tXUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQm+UVdSXqNWvjC8Si+yN46r6AkSU0yoCRJTfIWXw+thEt/Sf1nQDXIgBkd/y6lq5cBJWkoLYR9CzXoj8eAGuAvv5aCv1fSlfFDEpKkJhlQkqQmeYtPkv7IvO07HK+gJElNWrKASrItyYkkJ5PsWarXkST105IEVJJVwD8DfwtsBj6WZPNSvJYkqZ+W6gpqC3Cyqp6rqt8DDwLbl+i1JEk9tFQfklgPnB44ngb+anBCkl3Aru7wt0lOjOB1bwB+OYLnaZX9Xf363mPf+4P+9zh0f/n8yF7zL+YbXKqAyjxj9bKDqgPAgZG+aDJVVROjfM6W2N/Vr+899r0/6H+PLfW3VLf4poGNA8cbgDNL9FqSpB5aqoD6CbApyY1J3gDsAA4t0WtJknpoSW7xVdWFJH8P/BBYBXy1qo4txWtdYqS3DBtkf1e/vvfY9/6g/z0201+qauFZkiT9kbmShCSpSQaUJKlJvQioPi6rlOSrSWaSPDUwdn2Sw0me7bbXLWeNi5FkY5IfJzme5FiS+7rxXvSY5I1Jjib5edff57rxXvT3kiSrkvwsyfe74771dyrJk0meSDLVjfWmxyRvS/JQkme6f4vvbam/qz6gerys0teBbZeM7QGOVNUm4Eh3fLW6AHyqqt4F3Abs7n5ufenxPHBHVd0M3AJsS3Ib/envJfcBxweO+9YfwAeq6paB7wb1qccvAT+oqncCNzP3s2ynv6q6qv8A7wV+OHC8F9i73HWNqLdx4KmB4xPAum5/HXBiuWscYa8PA3/Txx6BPwF+ytxqKr3pj7nvNx4B7gC+3431pr+uh1PADZeM9aJH4K3A83Qflmuxv6v+Cor5l1Vav0y1LLW1VXUWoNuuWeZ6RiLJOHAr8Bg96rG7/fUEMAMcrqpe9Qd8Efg08IeBsT71B3Mr4Pxrkse75dmgPz2+HZgFvtbdpn0gybU01F8fAmrBZZXUriRvBr4DfLKqfrPc9YxSVV2sqluYu9LYkuTdy1zSyCT5CDBTVY8vdy1L7Paqeg9zbyHsTvK+5S5ohFYD7wG+XFW3Ar+jsduVfQiolbSs0rkk6wC67cwy17MoSV7PXDh9s6q+2w33qkeAqnoReJS59xT70t/twEeTnGLufyu4I8k36E9/AFTVmW47A3yPuf+poS89TgPT3ZU9wEPMBVYz/fUhoFbSskqHgJ3d/k7m3re5KiUJ8BXgeFV9YeChXvSYZCzJ27r9NwEfBJ6hJ/1V1d6q2lBV48z9m/tRVX2cnvQHkOTaJG95aR/4EPAUPemxqn4BnE7yjm5oK/A0DfXXi5UkknyYufvhLy2rtG95K1q8JN8G3s/c0vfngM8C/wJMAn8OvADcU1W/XqYSFyXJXwP/DjzJ/7+H8Rnm3oe66ntM8pfAQeZ+J18HTFbVPyX5U3rQ36Ak7wf+sao+0qf+kryduasmmLsd9q2q2tezHm8BHgDeADwHfILu95UG+utFQEmS+qcPt/gkST1kQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkpr0f25rHTUn4bgZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a plot of how many more precincts than tracts by county\n",
      "0 county has 4 more precincts than census vtds\n",
      "6 county has 6 more precincts than census vtds\n",
      "15 county has 10 more precincts than census vtds\n",
      "17 county has 13 more precincts than census vtds\n",
      "20 county has 12 more precincts than census vtds\n",
      "29 county has 51 more precincts than census vtds\n",
      "34 county has 9 more precincts than census vtds\n",
      "35 county has 1 more precincts than census vtds\n",
      "43 county has 1 more precincts than census vtds\n"
     ]
    }
   ],
   "source": [
    "n_bins = 21\n",
    "countyDifferential = [0]*nCounties\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "print(\"here is the histogram of census vtds by county\")\n",
    "ax.hist(countyNo,bins = n_bins)\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "print(\"... and here is the histogram of precincts by county\")\n",
    "ax.hist(vestCountyNo,bins = n_bins)\n",
    "plt.show()\n",
    "for t in range(nTracts):\n",
    "    countyDifferential[countyNo[t]] -= 1\n",
    "for p in range(nPrecincts):\n",
    "    countyDifferential[vestCountyNo[p]] += 1\n",
    "print(\"here is a list of how many more precincts than tracts by county\")\n",
    "CCCCC = [0]*nCounties\n",
    "for c in range(nCounties):\n",
    "    CCCCC[c] = c\n",
    "    if countyDifferential[c] != 0:\n",
    "        print(c,\"county has\",countyDifferential[c],\"more precincts than census vtds\")\n",
    "#plt.scatter(CCCCC,countyDifferential)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29618771-a993-4050-852a-177d7f04e15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have precincts with voters but no associated tracts.  We will build geoms county-by-county to understand why\n",
      "Here is county 0 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqPklEQVR4nO3de5wkVX338c+vunuue7+yy+6ywAIi4C6wIiD4CCICGhSRPCbB8ASVmKBBE4mYEMUkJkDwUZ7ESAjKizwYDSjIRQIicvGC4C4sy8Luyh32ws7e7zPT3fXLH1U90zPTs92zM9XdM/19v1796q5TdapOVXfXr+qcqjrm7oiISOMJal0AERGpDQUAEZEGpQAgItKgFABERBqUAoCISINK17oAQzFt2jSfP39+rYshIjKqLF26dJO7T++fPqoCwPz581myZEmtiyEiMqqY2Wul0lUFJCLSoBQAREQalAKAiEiDUgAQEWlQCgAiIg1KAUBEpEFVHADMLGVmT5vZvfHwFDN70MxeiN8nD5LvLDNbbWYvmtkVRekV5RcRkWQM5T6Ay4CVwIR4+ArgIXe/Ot6xXwF8oTiDmaWAbwLvBdYAvzGzu939+Uryi0h5hUe6u4MXpfV/0LsBZha/R5/7z8cdQnfC+L13OErzonFhv+kL+QtpTu88CuXxovlu2NlJczogZUYQGIH1lm/l+p2Mb0kzpb0pXq/edQT6rFvP+vckFN765emXt/hR+L1p0XsuDAFIB9azXXvWpd82L04fbL7FBd7XuqQCo60pxZ6uPLu7c+TyThAY45vTfOjYA2lKj2ylTUUBwMzmAO8Hvgr8eZz8QeDd8edbgEcYuAM/AXjR3V+O5/P9ON/zFeavC2HoXPPAKtZv6xzwJVfyoxvsC/d+mQf7gfafvvgHyIBxg/3oSywjLlv/+RYvr2RvEf3XvcRySpehePqBc97nPErk7TOHIfy5i7dFcTGK51s8bd/t0zvXUt9HYZo+ZdzHdKWWwaDLHph3uMyiwDCS85RkzJncyskLpo3oPCs9A/gG8JfA+KK0me6+HsDd15vZjBL5DgTeKBpeA7xjCPkxs0uASwDmzZtXYXFH1iubd/Nvj77MjPHNtDdHm6zn2Ml6PxeOqHqHC5NYn2FKTN8z7SB5epc3cBmVLr/4zQALwAjio8HeaYvLWTgi629AuUosv+86D9wG/ctZXNa+Zei3Hv3WZbDll15WvzJa32X23a5F6UUZite91HYrHGkz2LyK04vLM+i8SqcX/xYGL0f0uffItTewFB+VF46+A4uOxIMgmk/PsFk8vjetMH00Xe9vpTettyx90uLts2V3N/OmtGHW92zDHX62qoP509o5avYEUsFg32vfbdx3mqH9F/tPkwoMxwlD+qxL7zbtvz4D/zsD5lv8mxikXN25kM5cnramFO1NadIp4+nXt/GH33mSbDjyEbpsADCzDwAd7r7UzN49xPmX2ncMaS3c/UbgRoDFixfX5Bhl5fodAHzn/7ydow+cWIsiiDSU095S8niwIRUOOpNQyZzfCZxrZucALcAEM7sV2GBms+Kj91lAR4m8a4C5RcNzgHXx50ry14VV63eSCozDZo6rdVFEREZM2RYFd/+iu89x9/nAR4GfufuFwN3ARfFkFwF3lcj+G+AwMzvYzJri/HfH4yrJXxdWvbmDQ6e305xO1booItJwkqv4GE6T8tXAe83sBaKrfK4GMLPZZnYfgLvngE8DDxBdQXSbuz+3r/z1aOX6nbzlgAnlJxQRGWGFxvlS9enDNaTKJXd/hOhqHdx9M/CeEtOsA84pGr4PuK/EdCXz15vte7Os3baXC088qNZFEZEGVDj+D0q1MA+T7gQu48WOnQAcrvp/EamBML76J4H9vwJAOS9t3A3AodMVAESk+gpnAElUASkAlPHKpt1kUsacya21LoqINCBPMAIoAJTx6qbdzJ3SRjqlTSUi1Ve4c730LZnDo71aGXu680xoydS6GCLSqApXAekMoPpCd4IkKt9ERCqgNoAaip6TogggIrXRcx9AAvuh5B4yMcosX7ONTbu6ep4QGQTw9vlT4jMABQARqY2eNoAEdkMKAMCW3d2c+y+/HJD+Z6cvIHRPZMOLiFSibu4EHqs6s3kALj3tUM586wEAfPCbv2RvNk/okFIEEJEa6WkDUCNwMgobds7kNhbOncSrm6Obv7buyeLuBNpKIlIjnuCNANq10Xt9bf8erDKpgOVrtqsNQERqJskzAFUBUdRrUrypjz9oMgDfe/J1ANqa9BhoEakRtQEkq7BhC0f+c6e09Yz7zOkL+NhJehKoiNRG71VAqgJKRrxd73x67YBRu7pybNrZXeUCiYhECgemSdyQqgAATGptAuDZtdsHjLv5l69y0y9ernaRREQACHuqgHQjWCKy+bDPO8CBk1o58ZCpLHltC/kwuS7ZRET2pXAVkC4DTUhXLtrxX/zOg3vSMinj4dUdrN/e2fs4VhGRKkty96MzgCLzihp/LzzxIB797Ua6cyHvO+qAGpZKRBqZJ/g0UAWAQXzi1EP4xKmH1LoYItLw1B+AiEhDSvIMQAFARGQUUABImKu1V0QaiAIARXcC17QUIiLVpQBA0bOAFAFEpIEoAFD0NNAal0NEpJoUAACLt4LaAESkkSgAMPBpoCIijUABAFi3rROAaeObalwSEZHqKRsAzKzFzJ40s2fM7Dkz+0qcvtDMHjezZ83sHjObMEj+y8xsRZz3s0XpV5nZWjNbFr/OGbG1GqLVG3YCcNDU9loVQUSk6io5A+gCTnf3hcAi4CwzOxG4CbjC3Y8B7gQu75/RzI4GPgmcACwEPmBmhxVN8nV3XxS/7hvequy/Yw6cCMCH//VX/OsjL9KdC8vkEBEZ/coGAI/sigcz8cuBI4DH4vQHgfNLZD8S+LW773H3HPAocN6wSz3CDp7WzoePOxCAa+9fzb89+lKNSyQikryK2gDMLGVmy4AO4EF3fwJYAZwbT3IBMLdE1hXAu8xsqpm1Aef0m+7TZrbczL5jZpMHWfYlZrbEzJZs3LixsrXaD/9w3jH8399dSFMqYGdXLrHliIjUi4oCgLvn3X0RMAc4Ia7auRi41MyWAuOBAf0muvtK4BqiM4T7gWeAwt71W8ChRNVK64GvDbLsG919sbsvnj59euVrNkQtmRQfPm4OqcB46rWtfOuRl3h10+7EliciUmtDugrI3bcBjwBnufsqdz/T3Y8HvgeUrDdx92+7+3Hu/i5gC/BCnL4hDiwh8O9E7QQ1t2DGOJa8tpVr7l/FjT9XV5AiMnZVchXQdDObFH9uBc4AVpnZjDgtAK4Ebhgkf2G6ecCHiYIFZjaraLLziKqLau7uT7+TF756NjMnNJPLqzFYRMauSjqEmQXcYmYpooBxm7vfG1/eeWk8zR3AzQBmNhu4yd0Ll3X+0MymAlngUnffGqdfa2aLiBqUXwX+eCRWaH+4O1+841l+8vwGJrVmGN+aYfOubt0YJiJjWtkA4O7LgWNLpF8PXF8ifR1RY29h+NRB5vuxIZU0QTu7cnz/N28AsGV3N+86fDrvPmI65y6aXeOSiYgkR11CAmEYHep/6QNv5eJTDi4ztYjI2KBHQYiINCgFAJLpbFlEpN4pABRRm6+INBIFAOh5HrT6AxCRRqIAQG+XkCIijUQBQESkQSkAoB7BRKR+JblbUgAAzAqdwisCiEh9SuJqRQUAdAYgIo1JAUBEpEEpANB7FZBOAESkkSgA0Fu3piogEak3Se6XFACKqBFYROpVEvcrNeTTQLP5kIdWdpDNh4TurNm6t9ZFEhGpuoYMAI+/tJlP3bp0QPqsiS01KI2ISG00ZBVQVy7q6vHr/3thT9r9nz2V846dU6siiYhUXUOeARSq0hZMH891FyzkyVc2c9iM8TUtk4hIKUm2TTZkAChwnI8cP4ePHK8jfxGpb0k8s7JhAkBnNt/zOa/rPfeLu/c8NkNERr+GCAA/enotn/2vZQPSA+3MhuR933iMlzfuprUpxeS2Jm66aDGHz1TVmcho1RAB4I0tewC4/H1HANGR7LjmNEfOmlDLYo06L3Ts4ti5k5g3pY0fLVvHix27FABERrGGCACFCp9P/a9DSQU66t9f7nDKgmmcfcwsfrRsnXpSFqkC3Qks9cNMj8wQqYEkaqwbIgAUdlg6Yh0+o/eyNDWhiIxuDVEFJPvv1U27ufXXr5Xc2d/669f5xYubyKQCmlIBTemATCp6NaUDmlJGJhUwoTXDGUfOpCndEMcbIqNGQwQAHbHuvx8tW8tNv3iFlky0814wYxxzJrXx1lkTeKFjJ8+v30E2F9KVD+mO77Au5eY/ejunHTGjWsUWkQo0RgBQnfV+C0PHDFb93dl90u+77NQB07o7+dDpzodkc9H78+t3cNF3nqQrO3hwEJHB1bRPYDNrMbMnzewZM3vOzL4Spy80s8fN7Fkzu8fMSl5TaWaXmdmKOO9ni9KnmNmDZvZC/D55xNZq8HVJehFjzlB+fGZGOhXQ1pRmYluG6eObmT6uObGyiTSW2vQJ3AWc7u4LgUXAWWZ2InATcIW7HwPcCVzeP6OZHQ18EjgBWAh8wMwOi0dfATzk7ocBD8XDidAJwP5zV+O5yFhVNgB4ZFc8mIlfDhwBPBanPwicXyL7kcCv3X2Pu+eAR4Hz4nEfBG6JP98CfGh/VkCS5ejxDyJjVUVtAGaWApYCC4BvuvsTZrYCOBe4C7gAmFsi6wrgq2Y2FdgLnAMsicfNdPf1AO6+3sxKthCa2SXAJQDz5s2rdL36clcD8H4a7hlAYbt/6a4VfP3B39KcCWhOBzSnU7RkAsa3ZPjz9x7O3CltI1JeEalcRdfluXve3RcBc4AT4qqdi4FLzWwpMB7oLpFvJXAN0RnC/cAzQG4oBXT3G919sbsvnj59+lCyyghwhnf11IIZ47jkXYdw4iFTmT+tjantTWRSAXuzed7Yspc7n17LL17cNGLlFRlrPMGrWIZ0FZC7bzOzR4Cz3P064EwAMzsceP8geb4NfDue7h+ANfGoDWY2Kz76nwV07N8qVFBuVI9dzosdu/jpyg2kA6MpHZAOAjIp47dv7hzWfDOpgL8658iS49Zt28vJV/9sWPMXkf1XNgCY2XQgG+/8W4EzgGvMbIa7d5hZAFwJ3DBI/sJ084APAyfFo+4GLgKujt/vGv7qyP7614df5I6n15YcN318MlfyFI5rFJxFyqtVp/CzgFvidoAAuM3d740v77w0nuYO4OaokDYbuMndz4nH/TBuA8gCl7r71jj9auA2M/s48DpRO0Ii3HUJaDnZ0Dloahv3fOYUsrmQbN7J5kO68yFT25sSXba+GpHaKBsA3H05cGyJ9OuB60ukryNq7C0MD7xjKErfDLxnKIXdX0l2qTZWuDspMya0ZKq6TADTOYBITTTMw1m0i9k3h6pvJFcdkEhNNUQA0KMgKlDDG760/xepjYYIAKB65kpUu52k5zHd+nJEykriX9IQAUAnAOU5XvUj8Z6ntFZ5uSISaYgAAGpoLKcW1WS9ZwDVX7aINEgAUBtAedGlslVeZvyuACBSGw0RAADVM5QRVQFVuw1Al4GKlKNO4YepFvXbo021zwA6s3keXr0R0BmASCWSuFiiIQKAlFftWrKHVnbwd/c+D8CUhO80FpHSGqJLSGpQvz0aVfNyzM5sHoAffOokFs+fUrXlikivhjgDUBtwedVuKC8sbsb4luouWER6NEQAADU0llfddpKeBmB9LSL7lOSzzBoiACTZocJYUe1GYH0lIkOTxN+zMdoA0JFmOcPt+asSe7vzLH1tK6nAeGXzbqjCMkVkcA0RAHS0WZ578vcBfOeXr/BPD6zuk9aaSSW6TBEZXEMEANB9YJVI+mh8V1eOVGDc+vF3kM2HTGzNMHVcMr2NiUh5DREAdAJQXjW2kTukzDjp0KlVWJrI2KA7gUeAHjm8b16F/gC8lp0OiIxySezCGiIAqA2gvKhHsIT3ztr/i9SVhggAoB1POVEjcMLLQFf9iNSThggA6hS+MomfAFThSiMRqVxDBABApwBlVKUNQM9kEhkyNQIPk9oA6oOjOCyyv5I4e26IAADa8ZTjeOJXSkVnAPomROpFwwQA2bdqXQaq3b9I/WiYAKAjz32rRv28qw5IpK40TACQfatWn8Da/4sMTZJNmAoAAlSnodw9+XYGEamcAoAAhTuBk1+G9v8i+6cmj4IwsxYze9LMnjGz58zsK3H6QjN73MyeNbN7zGzCIPk/F+dbYWbfM7OWOP0qM1trZsvi1zkju2oyVFW5DyDhZYhI5So5A+gCTnf3hcAi4CwzOxG4CbjC3Y8B7gQu75/RzA4E/gxY7O5HAyngo0WTfN3dF8Wv+4a3KjIs1WgErsKlpiJSubIBwCO74sFM/HLgCOCxOP1B4PxBZpEGWs0sDbQB64ZVYklENRqBdQYgMnRJdmlbURuAmaXMbBnQATzo7k8AK4Bz40kuAOb2z+fua4HrgNeB9cB2d/9J0SSfNrPlZvYdM5s8yLIvMbMlZrZk48aNla6XDFFVLgNFbQAi9aSiAODueXdfBMwBTjCzo4GLgUvNbCkwHujuny/eqX8QOBiYDbSb2YXx6G8BhxJVK60HvjbIsm9098Xuvnj69OlDWDUZimp1CKNzAJH6MaQewdx9m5k9Apzl7tcBZwKY2eHA+0tkOQN4xd03xtPdAZwM3OruGwoTmdm/A/fu1xpIxZav2cZz63aQSQVkUkY6iN4zqYDte7PMnJB094yuMwCROlI2AJjZdCAb7/xbiXbq15jZDHfvMLMAuBK4oUT214ETzawN2Au8B1gSz3eWu6+PpzuPqEpJEnT57ctZvWHnoOMPnd6e6PLVBiBSXyo5A5gF3GJmKaIqo9vc/V4zu8zMLo2nuQO4GcDMZgM3ufs57v6Emf0AeArIAU8DN8Z5rjWzRUS1D68CfzxC6ySD6M6HnHHkTL78O2+lOx+SyzvZfBi/nCNmjk90+XoctMjQJVk9WzYAuPty4NgS6dcD15dIXwecUzT8ZeDLJab72FALK+Vt3tXFV3+8kt3dOdyjH09U9+68ub2TYw6cyNwpbTUpW7UeNyEilRlSG4DUv6de38YdT6/loKlttGZSAARmmMGhM9o5/S0zalY2nQGI7L8k/jsKAGPUN3//OI4+cGKti9GHHgYqUl/0LCCpGnUII1JfFACkarwqdxuISKUUAMaYJG8bHza1AYgMnTqFl7FAj4IQ2X9JVJ8qAEjVuOsyUJF6ogAgVaMzAJH6ogAwxtRxC4AeBSFSZxQAxqh6PNKOzgDqsGAidSzJq+cUAKRqojYAEdkfSfx3FACkatQdgEh9UQAYY+r5NgDUBiBSV/QsoDGq1OWWl373KX710qa4Q5jezmAGfE4HZALr/ZwyMkFAJh2lNaUC0kXTtzelOP/4OeztzrN8zXZSKSMdRB3OpIs+7+jMqg1ApI4oADSQpa9tZUp7EyccPIVsUV8A3TknF8b9AuScvXuz0XAunqboc3E/Armw93RjQmuGn67cwH3PvrnPMhxTZw+oE6l3SZ7VKwA0EMd5+/wp/OOH3zYi8wtD57UtezjtukfIhc6e7jwLZozj2o+8jXwYBYl86ORCJ5d38mHI4Ql3OiMyVulx0DIsI30kEQRR9Q701u23N6U4bt7kkV2QiCRCjcANZqSPIgpBxczquwFaRAZQAGgg0f55ZCOA95+rGnlFRg0FgAaSxBF67xnAyM9bRJJ9vIsCQIMZ8Sqg+H3L7m46s3ld5y+SkCSepKtG4Bp67Lcb+dPvPkV3PsTdcYdxLWnuv+xdHDCxJYEljvyjGJrT0THE3/94JQAnzJ8ywksQkaQoANTQCx272NWV44/eOZ+WTIrXt+zhx8vX8+aOzkQCQBJVQLMntXLrx99Bx85OsvmQRXN1BZDIaKEAUEOF7hs/e8bhTGzN8PCqDn68fH2iy0yirv6Uw6aN/ExFJHFqA6gD1WpAdZKpRxSR5CR5eXXDBICwDi9S77mCpmrLq79tICK10xABoCkdkMvX386v5xr6fqcASe6odbmmyOiUxH+3IQJAczqgM5evdTEGqPoZQJWWIyKjQ9kAYGYtZvakmT1jZs+Z2Vfi9IVm9riZPWtm95jZhEHyfy7Ot8LMvmdmLXH6FDN70MxeiN8Tu3zEqM/n5BeKFBRCexUigU4ARKSgkjOALuB0d18ILALOMrMTgZuAK9z9GOBO4PL+Gc3sQODPgMXufjSQAj4aj74CeMjdDwMeiocT4UBQh3s+3UUrIuXUtE9gj+yKBzPxy4EjgMfi9AeB8weZRRpoNbM00Aasi9M/CNwSf74F+NBQC1+p0L0uOiLpzOa58bGXWPLqFqA+G6ZFpHFUdB+AmaWApcAC4Jvu/oSZrQDOBe4CLgDm9s/n7mvN7DrgdWAv8BN3/0k8eqa7r4+nW29mMwZZ9iXAJQDz5s0byroVlWPkzgB2d+XIuzOhJTPkvI+/tJl/uG8VU9qbuPczp9CxoxMYeAawYUcXa7buITBjXEuacU1pgqIVCEOnO+7MJbq0M2pINqA7Hw66fANe37KHh1d3RD18pYxMOurh68BJrUxubxryOolIdSRxCFtRAHD3PLDIzCYBd5rZ0cDFwP8zsy8BdwPd/fPF9fofBA4GtgG3m9mF7n5rpQV09xuBGwEWL168X4fMoY/M9e/b92RZ+Lc/IZMyVv7tWaRTQ2tDv+4nq4HouTknX/2znvRCG0DhsQqfunXpgLxN6YCWdEAQGNv2ZMsuK1Ui4k1qa+Lh1Rt5ePXGAePmTmnl5395emUrIiJjwpDuBHb3bWb2CHCWu18HnAlgZocD7y+R5QzgFXffGE93B3AycCuwwcxmxUf/s4CO/V+NsuUekXr2rXuiGJfNO3n3Id9G3d4U5bj2I28jDKN5zJncRiYOJCfMn8INFx7Hrq48oTth6OzszLGrK0dnLk9XNqQrFzJ9XBPNmRRNqQCz6AzH8fgdJrRkOGzGuAHLv/NPT2bN1r3k4t66srmoi8e/uWsFW3YNiN8iMsaV3YeZ2XQgG+/8W4l26teY2Qx37zCzALgSuKFE9teBE82sjagK6D3Aknjc3cBFwNXx+13DXptBOIM3tIahs7Mrx8TW8lU6l//gmZ7PhrGnO8cTL2/pqcvPpAJOPGQqTelBzgwM3nHwFH538YDaMgDSqYCzjp5Vthz7a1JbE5PaBlbzvOctM7nz6bWJLVdE9l+t+wSeBdwStwMEwG3ufq+ZXWZml8bT3AHcDGBms4Gb3P2cuK3gB8BTQA54mrg6h2jHf5uZfZwoUFwwYmvVTxh676WWRd7c3smJ//gQAEuuPINp45r3OZ/iqhfH+acHVnPzL1/tM83XLljI+cfPKZnfqM9r8XWHsEhjKhsA3H05cGyJ9OuB60ukrwPOKRr+MvDlEtNtJjojSFyhobSgM5vngefeZNkb23rS1mzdOyAA/PrlzezuyjFrYivzp7XxQseunnG/enEzty9Zw/uOmsmlpy1g484uPn7LEvZmB7/hrFBdU4/q4CIpEdkXdQq/f0J3OnMhl373KfKh89CqDWT7PRri6v9eyYzxLQQWNco67LNa5MofrWBXV44/efcC3jZnEh07oyt6vnTXCp5+fRuBwaWnLWD+tPa+Ges0AIhI42mIAHDC/Ck8+tuNrN6wk5QZC2aMZ+POLsY1p8ikAvZm83Ts7GLDjq6o8dWdcJCrKa98/5Hc8dRa9nTn+J2Fs1k0dxIAE1szHHPgRN7c0cmvXtrE+u2dHHHAeD5x6iE9eQ3DGfwyzVpRTBJpTA0RAM4+ZhZnHzNyjavFO/WC5nSKez5zCgA7O7Mcc9VPBkxTz9UsdVw0kYamPoHHkHptAxCRxqMAUEVm9VndoqAkUv/UKfwoUXju0JvbO3lu3faeHeyq9Ts5aGpbDUs2uHp4VpKIVJcCQALSgZEOjJt+8Qo3/eKVPuOOnFXyqdkiIlWnAJCAlkyK2z91Eht2dAKGWe8D246arQAgIvVBASAhx85LrH+bEZfk88ZFZJgSbKRTI7AAugxUpN6pT2ARERkxqgIao/Z25/niHcvZ052nvTnNZ884jIOmtpecVpeBijQmBYAx6oWOnfxo2ToOmNDCmzs6WTR3EhedXDoAQH3fpSwiyVAV0Bj3F2ceXusiiMgwJHmCrjOAUea6B1bzH4+/Gv0o4l+G0/tM/+gz5MNouNAPgsc9jAUluopUDZBI/atZn8BSP5a9sY3mTIr3xw+3i+4xsJ4qHIvTAjPam9OcdOhUAK6653m+8dALPHr5aYP0fqY6IJFGowAwimzd3c2OzizzprRx1blHVZzvK+cexc9f2MhPV3bwyOoOpo1rJh/3SZwOjCde3kx+sOdfi8iYpQBQh3L5kL+4/RkmtWY49bDpnHjoVMY1p/nkfyxh+ZrtnLJg2pDmd9HJ85k5oYWfruzgsu8vKzlNvT6jSESSowBQhzbv7uauZesAuOXx10gHxnEHTWbJa1s5YuZ4rrtg4ZDn+d63zuS2Pz6JXBiSMiMVGEFghKHTnQuZM1kBQKQe1bpTeKmRq37nrRw+czyPvbCJn7+wEYADJ7dywMSWIc8rFRgnHDxlpIsoIlWSxBN7FQDqWFM6xckLpnHygmlccfZbWLl+B+Oa9ZWJyMjQ3qQOFeJ8/4e06VHSIjKSdCOYiEiDUgCoY3pGj4i4HgfdYHRPlohUgQJAHUoH0ddy9X+vojunG7REJJnjQgWAOjS5LcPb509mV1eOC7/9BNv3ZmtdJBEZg3QVUI3t7srxwHNvkss7FPUd/NG3z+OVTXt48pUt3L7kDT5x6iG1LqqIjDEKADV27/J1fOGHz+5zmsKTPUWk8dT0cdBm1gI8BjTH0//A3b9sZguBG4BxwKvAH7j7jn55jwD+qyjpEOBL7v4NM7sK+CSwMR73V+5+3/BWZ/Tpiuv47/3MKT1P6XSP7gEoNP7Pm6LHNIjIyKvkDKALON3dd5lZBviFmf038M/A5939UTO7GLgc+JvijO6+GlgEYGYpYC1wZ9EkX3f364a/GqPfrIktTB3XXOtiiEidqkmn8B7ZFQ9m4pcDRxCdGQA8CJxfZlbvAV5y99f2s6xjUuEoP4nnfIiI7EtFVwGZWcrMlgEdwIPu/gSwAjg3nuQCYG6Z2XwU+F6/tE+b2XIz+46ZTR5k2ZeY2RIzW7Jx48ZSk4xqSd7kISKyLxUFAHfPu/siYA5wgpkdDVwMXGpmS4HxQPdg+c2siShY3F6U/C3gUKIqovXA1wZZ9o3uvtjdF0+fPr2S4o4aD63cwFX3PA9AiZ4aRUQSfSLAkO4DcPdtwCPAWe6+yt3PdPfjiY7sX9pH1rOBp9x9Q9G8NsSBJQT+HThhqIUf7Z5bF7WZf/7MwwfpplFEJDllA4CZTTezSfHnVuAMYJWZzYjTAuBKoiuCBvN79Kv+MbNZRYPnEVUpNaQ/efcCtQGIyD5ZAvcCV3IGMAt42MyWA78hagO4F/g9M/stsApYB9wMYGazzaznck4zawPeC9zRb77Xmtmz8XxPAz437LUREZGKlb0M1N2XA8eWSL8euL5E+jrgnKLhPcDUEtN9bKiFFRGRkaM7gevADY++hFnU2BOGTugQuuPe+zl04uHoM0BzOqC9OU0qMDqzeaC3wain3ShO8L6DPZ3NuENnNuTh1R1MbW/ioKntNKUDmuNXUzqgKRX0pDWlUzSlAzp2dtKSTrG7K8fWPVm27O5ifEuGUw6bxoGTWmlOB2RSAZPbmzCDX724iVzoNKdTtGSCfb43pwMCtYrXnLuTzTu5MCSbc7rzIdmiV3fOyYdOvvC7jH+7+TD67eYLv98wmi6Mp8uHkHcnH4bkw2h8rjCf+HMYD+fDfi930oGxdtteunMh+TAqYz4MyYVOLh9Nl+s3HLqTCoymdEA6MNKp6D0VWM979Dno+VwYFxRN03c4IGVGOhVPH/e1XWo4sL7zLjW/ILDoJlCP/p1hGL2/unl3Yt+xAkANzZ3SCsA/PbC65HgzCMwILLpPIIiHC7vGrlz0Iy+n0LxgPcPWbxiOmj2R3d15fv3yZrpyebpyId25kO58WPYqhHHNaSa3Z9i0s5v//+uRuc2jKRXQ2pRiQmuaia0ZJrZmmNCS6f0cv6L0dJ/0pnTQ8+czo+hzdYJKGDrZMIx2nvloG+byHu84e3eo2TAkG3+HfafpzZsNPZ4mSuvZyXrvTrKw882H0cFC8Tz6f+6Ol1f4XLys7j7TR+n1JLCob+ts3pna3sTE1ky8sy3s1Ht3rO2ZdM8OPR3/DqKA0HdbFIJMIVD0DIchYQi5MOwJPrmi90KgqpZMKgpeI00BoIbOO3YOZx0VtYUX7+wDi36w5XZY7k5XLiR0pyWd6t3Rj+COzj36ofcEhJ7AkGdyWxPjWzI9P8zObJ7n1++gY0dXdLSYC9m6p5udnTlOOWwak1ozdOVCOrP5nvfObEhXrvT7nu4cO/Zm2b43y47OHBt27Io+7832PEJjqIKe7Rw9ea94OE4iCKxP4I0e0BflT5n1jM+HvTvMXD7eicc7iWoo7BAL5Y8+Ex/lBmTSRiYVncFlUgHpVDTcmkkxoSVNJk7PxOnpVBCfuUVHyFFe650uHQ1H844+p4KAVBBtp/5Bt3g79i9nz8uMVMri6aNHoRd/DoKBAXxnZ5b2pnTNzxILZ+i9ASLsc7aS6/c59L5nKMXD+fh/VjjA690HRI2/MyY009qUGvF1UACoseF8qWZGS2bkfxT9l1HYQVDmSRUtmRTHzSt5P9+I68zm2bE3y47OKEBEgSHH9r1ZsvkwPu0nrnKIXk7farQweuhSn2F3Sla9Fc6CCkfeUZVCQFM63iH22ZHagB1rJugd11RIK9q59qZH82sqMZ/inWYlBwhj1fiW+rhkOgp6UXCLJPtfTIICgIxKLZkULZkUMya01LooIqOWOoQREWlQCgAiIg1KAUBEpEEpAIiINCgFABGRBqUAICLSoBQAREQalAKAiEiDstHUJaGZbQSq1afwNGBTlZY1UkZjmWF0lltlrg6VeWQc5O4DulQcVQGgmsxsibsvrnU5hmI0lhlGZ7lV5upQmZOlKiARkQalACAi0qAUAAZ3Y60LsB9GY5lhdJZbZa4OlTlBagMQEWlQOgMQEWlQCgAiIg2q4QKAmV1gZs+ZWWhmi/uN+6KZvWhmq83sfUXpj8Rpy+LXjEHmXTJ/LcpcNP5uM1sxyHwzZnaLmT1rZivN7Iv1XuZ4/NvM7PF4/s+a2Yj0CpNkmeNp5pnZLjP7/EiUN8kym9l7zWxpvH2XmtnpI1XmJMtdSf5qltnM7jezZ+J8N5jZgG7DkvwflhV1d9c4L+BI4AjgEWBxUfpbgWeIOj48GHgJSMXj+kw7yHwHzV+LMsfjPwz8J7BikPn+PvD9+HMb8Cowv87LnAaWAwvj4an1vp2LpvshcDvw+Vr+nivczscCs+PPRwNrR6rMCZe7rv6HwIT43eLv/6Ml5pvY/7Dcq+HOANx9pbuvLjHqg0RfQpe7vwK8CJwwhFkPN/+g9qfMZjYO+HPg7/c1a6DdzNJAK9AN7KjzMp8JLHf3Z+LlbHb3fJ2XGTP7EPAy8NxIlDXpMrv70+6+Lh58DmgxszK9Qte+3PvKX4syu3vh/5QGmoj+cwNmTUL/w3IaLgDsw4HAG0XDa+K0gpvj6p+/MSvZG3e5/EnY1zL/DvgasGcf+X8A7AbWA68D17n7lgTKWWy4ZT4ccDN7wMyeMrO/TKaYfQyrzGbWDnwB+EpSBSxhuNu52PnA0+7eNXLFG9Rwy11v/0PM7AGgA9hJ9J/rrxb/Q2CMdgpvZj8FDigx6q/d/a7BspVIK0TrP3D3tWY2nug07mPAfwwhf1kjWWYzWwQscPfPmdn8fSz2BCAPzAYmAz83s5+6+8t1XOY0cArwdqIdwUNmttTdH6rjMn8F+Lq77yp97LBvNSpzYdlHAdcQnXkNSY3KXTf/w54P7u+L26m+C5wOPNhv2mH9D4djTAYAdz9jP7KtAeYWDc8B1sXzWxu/7zSz/yT6wvoHgEHz16DMJwHHm9mrRN/xDDN7xN3f3S//7wP3u3sW6DCzXwKLiaoq6rXMa4BH3X0TgJndBxwHVBQAalTmdwAfMbNrgUlAaGad7v4vdVxmzGwOcCfwh+7+0lALUMPfR738D4vn22lmdxNVF/UPAMP6Hw5LNRoa6vHFwIaco+jbkPMykCL6sU2Lp8kQna59qsT8SuavRZn75ZnP4A1mXwBuJjqCaQeeB95W52WeDDxF1FiWBn4KvL+ey9xvuqsYwUbgBLfzpDj/+SNd1oTLXTf/Q2AcMCueJg38F/DpEvNL/H846LpUYyH19ALOI4rYXcAG4IGicX9N1IK/Gjg7TmsHlhJdefIccD29LfznAn+7r/y1KHO/vH3+LMVljn+gt8fr9Txweb2XOR6+MC7zCuDa0VDmovSrGNmrgJL6bVxJVC+9rOg1o97LXUn+apUZmAn8ht59xz8D6RLbOrH/YbmXHgUhItKgdBWQiEiDUgAQEWlQCgAiIg1KAUBEpEEpAIiINCgFABGRBqUAICLSoP4HHNXGnVq+wHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 4 more precincts than census vtd tracts for county 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 0 unionized from precincts, with 4 unmatched precincts center-numbered\n",
      "2nd plot above is same county 1 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 2 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 3 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 4 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 5 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 6 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 6 more precincts than census vtd tracts for county 6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 6 unionized from precincts, with 6 unmatched precincts center-numbered\n",
      "2nd plot above is same county 7 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 8 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 9 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 10 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 11 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 12 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 13 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 14 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 15 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 10 more precincts than census vtd tracts for county 15\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABPQElEQVR4nO3dd3xUVfr48c8zM+kJJYGEQBJCb6EjzQULIG3B3gvI2lZBxP3Kulb4ubuiq+vCKrgqIvYFBVF0QUQRUTrSQxMCJIQUWkjPzJzfH/fOpDdSJuW8X6+Q3Dv33jlnQu5zTxelFJqmaZpm8XQCNE3TtLpBBwRN0zQN0AFB0zRNM+mAoGmapgE6IGiapmkmm6cTUBktWrRQ0dHRnk6GpmlavbJ9+/ZUpVTL8o6rVwEhOjqabdu2eToZmqZp9YqIHK/IcbrKSNM0TQN0QNA0TdNMOiBomqZpgA4ImqZpmkkHBE3TNA3QAUHTNE0z6YCgaZqmAfVsHIKmaVpJ0rLz+GDjcfIcTmwWwWqxmN+NL5tVGNUtjNAmvp5Oap2mA4KmafXejwdT+Mfqg2Uec/xMJk+N61ZLKaqfdEDQNK3ey7U7Afj+T1cQ0dwfp1LYnQqHQ2F3Ohn+8g/YHXoxsPLogKBpWr3nMFd+9LZZ8LYVbxp1KrDqFtNy6Y9I07R6z+E0AoLNUvItzaEUFovUZpLqJV1C0DSt3nhh5X4OJV3EIoJFQMzv8eeyALCWctNXSvHFrwlsjzuHRQQRjGtYMLeN6xjXLfCzxfUe5b2e/5oIWMVozL6xfwSdw4Jq8yOqEh0QNE2rN979+RihQT60auqHUgqnUjiN5gOu6NyS5v5eJZ537+XtiE1MQymMc5TC4VTkOcxrKMzr4d52OpX7WFVwv7ntcBY9t/Dr6Tl2svMczL42phY/oaopNyCIiC+wHvAxj/9MKfW8iPQG3gQCgTjgTqVUWgnnTwfuBwR4Wyn1L3N/MPBfINo8/xal1Lkq50jTtAbJ6TRutHcMbMv0kZ0qda4nehf1/X/fUt+asSvShpADXK2U6g30AcaIyGDgHeBJpVRPYDnwRNETRSQGIxgMBHoDvxcR12/ySWCtUqoTsNbc1jRNK5Gr4Vg3Dteccj9aZUg3N73MLwV0wSg5AKwBbizh9G7AJqVUplLKDvwIXG++di2w2Px5MXDdpWRA07TGwdVwrBuHa06F2hBExApsBzoCbyilNovIXmAisAK4GYgs4dS9wN9EJATIAsYBriXPwpRSiQBKqUQRCa1STjRNa9DyexLVn4CwNjaZ+HNbUUqhAKWMp2mLGPmwiDGKuuDI6sLfLVgtEOTrxX3D2uHvXbPNvhW6ulLKAfQRkWbAcrMqaAowT0SeA74Ecks4L1ZEXsIoQaQDuwB7ZRIoIg8ADwBERUVV5lRN0xoQV5WRRepHQPh9r9bsOHGO5IvZCEbvIwEQAdfAOafx3eksuO1073c4FDkOJ7l2J32jmjGsU7nLIldJpcKNUuq8iKwDxiilXgGuARCRzsD4Us5ZCCw0j/s7EG++lCQi4WbpIBxILuX8t4C3AAYMGFDf2mg0TTNl5Tp45duDZOTYi3TVzO/aaXQHLakrKOQ4jO5E9aWE8MJ11dO7aMuxs9zyn40INZ/vivQyagnkmcHADxgJvCQioUqpZBGxAM9g9Dgq6XzXcVHADcAQ86UvgUnAHPP7iqpnR9O0umrvqQss3HCMZv5eeFkthbt5Okvo2knhrqBKGcEgukWAp7NSq/LbTmr+vSpSQggHFpvtCBZgiVJqpYhMF5FHzGOWAYsARKQ18I5Sapz52udmG0Ie8EiBrqVzgCUi8gfgBEY7hKZpDZTrxjb/zn4M7dCi0ucrMyg0tkZlp6t3VS1UlZUbEJRSu4G+JeyfC8wtYf8pjMZj1/awUq57BhhRmcRqmlZ/OZ1VawMQs0qpsXEF0tJGYVcn3aNX07RakT+OoBHe1avA3ZiuA4KmaQ2F+aBbb3oJ1RWuklVtVBnpgKBpWq3IrzLycELqGV1lpGlag1ObN7aGxFmL4y90QNA0rVbU5o2tITGHX+gSgqZpDYcOCJemNif10+shaJpWK1yNyrrKqHyr9p5mzv9iAcjIdQC1E0h1QNA0rVY4dKNyhW2NO0v8uSzG9wrH4VQ08/ciKti/xt9XBwRN02qFsxb709d3DqfCz9vK3NuKjQmuUboNQdO0WqHbECrOqZRHqtZ0QNA0rVa4e8vogFAuh1N55HPSVUaaptUKZy3O2lnXfbc/id0JF9zTe1strqm/jam+9yemeaRqTQcETdNqha4yyvfsir0kXsgu85h+Uc1qJzEF6ICgaVqt0JPb5ctzOLl9YBR/vz4Gh7Pwmg/GmhAKPy9rradLBwRN02qFaxyCLiCYbQQWY0pvm7XufCC6Nk/TtFpRm7N21nWeajQujw4ImqbVCj25XT5nHV35TQcETdNqhatRWergk3Ft0yUETdMaNaduVHZzeGjgWXl0QNA0rVbkr5jm2XTUBUopXWWkaVrjlT+5Xd27EdY2XWWkaVqj5tQBATBKB7pRWdO0Rk2vh2Bwfw51MDDqgKBpWq1wKL0eAhTsfuvhhJSg3CSJiK+IbBGRXSKyT0Rmm/t7i8hGEdkjIl+JSJNSzp9hnrdXRD4REV9z/ywRSRCRnebXuOrNmqZpdYnTqRDR3U7r8roQFYlROcDVSqneQB9gjIgMBt4BnlRK9QSWA08UPVFE2gCPAgOUUjGAFbitwCGvKaX6mF/fVC0rmqbVZU5VNxtSa5ujDo/YLjcgKEO6uellfimgC7De3L8GuLGUS9gAPxGxAf7AqSqlWNO0esmhVKNvUIa6PclfhWqxRMQqIjuBZGCNUmozsBeYaB5yMxBZ9DylVALwCnACSAQuKKW+LXDIVBHZLSLvikjzS8+Gpml1nVJ6LQSo272tKvTrUUo5lFJ9gAhgoIjEAFOAR0RkOxAE5BY9z7zJXwu0A1oDASJyl/nyAqADRjVUIvBqSe8tIg+IyDYR2ZaSklKJrGmaVpfU1b73ta0uz+lUqemvlVLnRWQdMEYp9QpwDYCIdAbGl3DKSOCYUirFPG4ZMBT4UCmV5DpIRN4GVpbynm8BbwEMGDBAVSa9mqZVXeKFLDJzHVhEEIwnWxGjUdS1bRHAveKXsS0IYsF9XkaOvdE3KEOB3lb1MSCISEsgzwwGfhg3+ZdEJFQplSwiFuAZ4M0STj8BDBYRfyALGAFsM68brpRKNI+7HqMKStO0OiQ2MY2xc3+qtus19/eqtmvVV846vLZ0RUoI4cBiEbFiVDEtUUqtFJHpIvKIecwyYBGAiLQG3lFKjVNKbRaRz4AdgB34FfNpH3hZRPpgNFDHAQ9WU540TasmZzOMmuC7BkcxoG1woVW93N9xrfIFqPzVv5zKGJVb8LgurYI8mZ06Ib9R2cMJKUG5AUEptRvoW8L+ucDcEvafAsYV2H4eeL6E4+6ubGI1Tau4pLRspn3yK1m5jku+RnqOHYDr+rRhQHRwdSWtUavLjcp6CU1Na6BiE9PYcuws/ds2p6nfpVXVtAzyoWebpvrJvho1mEZlTdPqD1cPjKfHd6NflO7VXVfU+3EImqbVQ+71B+rejacxq8tVRjogaFoD5V6y0sPp0ArTJQRN02qded+hDj6INmp1eaEgHRA0rYFytSHUxRtPY+Yeh6BLCJqm1RZXlZFWt9TlcQh1MEmaplUHXWVUN7mqjOriNB46IGhag1V366obM+UqIdTB34sOCJrWQDl1CaFOqssD03RA0LQGSulxCHVS/trSde/3okcqa1oDVR3jEOLPZfLiNwfIdTjzp7q2GPXfrmmu3dNhF9p27cufErukYwqeX3DbImW/h5R3jKVAOih+Xkn56BPZjCDfwlN8KKWIP5eFUrin/C6UZlzfC+/DzLvr/V3pAsjJM7oZ1cUSgg4ImtZAufoYVeVBdMuxs3y9J5EOLQPwslrcs5gWnO3UqZS7K6V725zp1D3zqbPg8aAofoynO0XdPbgtL1wXU2jf+xuP8/yX+2rk/bysOiBomlZLXI2XVenN4mqHeO/egUQG+1dHskqlVOGg4QoSruDhVArlLCPouI5X5R9T9LUHP9hGVl7xWWFT03MAePXm3sXOdZhvZs767b6WIv+zz89DfroAAn1s9GzTtEY/z0uhA4KmNVDubqdVuIa72qkWHmbd1UwemGzDapESe/0oZbx2Y/+IWk+TJ+hGZU1roBRVLyG4nnTrYgNodXKqkpe0dCrVqOaC0gFB0xqo/F5Gl34Np/saDfu26HSqEj8nRePqtqsDgqaVYsqUKYSGhhITk9/QuHTpUnr06IHFYmHbtm3Fzjlx4gSBgYG88sor7n1PP/00kZGRBAYG1kq6XdzjEKrwjOt0lxCqI0V1l0OpEnv9KFW1z6++0W0IWqPlcDiY/sFiki86MSoGzDuoMm4AyYEt6TnpIbZ9uJAb5y3E26q4t2c0y5Yt48EHS14CfMaMGYwdO7bQvgkTJjB16lQ6depUk9kpRlVD/X/+4LaGfVN0OhXLf01gw+HUAt1bhZT0nEY1f7gOCFqjlXwhmZUHwmjuc56mvsbawUJ+S6yldTcyz6XiUELSRS/iL4YwtkcW13TpUuL1vvjiC9q3b09AQECh/YMHD67RfNQk1UhKCI+O6MSehAsFejcZXWnbhvjTNbyJp5NXa3RA0Box4y43eZCN6ePuLPGIuLg4fv/1e/zn7oGMn/+bu6G2qIyMDF566SXWrFlTqLrIk1z1/lXp31+XV/eqTvcNa+/pJNQJug1Ba7SkGm6YLs8//zwzZsyo9XaCsrju4VWZBruxNCprBl1C0BotEeN5qLSn/sLHUuaxmzdv5rPPPmPmzJmcP38ei8WCr68vU6dOrbb0Vpa7hFCFa7jHIehHx0ZBBwSt0XIFhIoVEcp+Qv7pp5/cP8+aNYvAwECPBgOonhKCniCvcSk37ouIr4hsEZFdIrJPRGab+3uLyEYR2SMiX4lIiS0vIjLDPG+viHwiIr7m/mARWSMih83vzas3a5pWHmX+a9zsinYzvf322+nbty/79u2jR1RXzq3/gG3rNxIREcEvv/zCkCFDCAoKonfv3ixfvtx91b179/Lqq6/So0cPZs6cycyZM4mIiCAzM5OIiAhmzZpVK7mrjiqxxtLtVDNUpCCYA1ytlOoN9AHGiMhg4B3gSaVUT2A58ETRE0WkDfAoMEApFQNYgdvMl58E1iqlOgFrzW1NqzXiDgiGyZMns2rVKvfrn3zyCb/88gsHDhzgssED8e80mP7DBhEfH8/58+fJysri4sWLrFq1igcffBC73c6ZM2fYsmULu3fvZt++fSQlJTF69Gji4+NxOp3Ex8fXXkAwv6uqlBCqJylaPVFuQFCGdHPTy/xSQBdgvbl/DXBjKZewAX4iYgP8gVPm/muBxebPi4HrKpt4TSuJ3e7g7f9t5dVlPxN7PKn04xzGZGZe5uPv8OHDCQ4OLnRMt27d6FKgm6nr3urv74/NZtS4Zmdnu5/Gjx49SufOnWnZsiUAI0eO5PPPP6+ejFVSdbQh5AeVKidHqwcq1IYgIlZgO9AReEMptVlE9gITgRXAzUBk0fOUUgki8gpwAsgCvlVKfWu+HKaUSjSPSxSR0CrnRqtxF7PzSL6Yg7+3lfCmfp5OTjF2u4OF327nxfUpAKyJ3cjz1/dxz0nvmr8eIPmC8WySnX2Gkyd3AnDq1Cny8rI5eXIX4EQpJ+AgNy/DOOeicCT5IiLC7h3b+PP0h0mIP8HcN98hNcNO09AI9sce4Nd9B4mMjGTp58uw5+WRnefAIoLVnE9fmbNlOpzGl92cJtPVaO26Abvuw66n/Pxt1+sqfzxdkc/CNVNnVdoQXJ/V7vgL+HlbSz2uvFJIZLA/LQJ9LjkdWu2oUEBQSjmAPiLSDFguIjHAFGCeiDwHfAnkFj3PbBe4FmgHnAeWishdSqkPK5pAEXkAeAAgKiqqoqdpNSDP4WTYyz9wPjMPEdj29EhC6tgf+exP1/PB3kz39oGLXtz+ftnz2edlrePQ4VcBOH06j9zc0xw6fEPhY3KNksabG714c+P6/Beu+wfNUk/yyMzneGGnD2Lzxj5kCoNHTQQRfNp0w37+DF2fXYWnHD+TSddWlza4yt/buEXc/vamKqWha6sgVj02vErX0GpepXoZKaXOi8g6YIxS6hXgGgAR6QyML+GUkcAxpVSKedwyYCjwIZAkIuFm6SAcSC7lPd8C3gIYMGCALrh6yL5TF9h67CznM/MA4wn1XGZenQsI8clnAV/+ProNncKD2f7baff89AWfrhXgdGaQm/M1gyI64+/9O0CRkX4Gi+VtmjaZBlhALAgWfGz/ZFTTHQwc2p+2bTsUWhjGqXoxa+di7ujpRXTXGJwqBqdzCg6nYt2Kjzkdf5zrxnTB6VQ4nEbJwCJgswgWixjfzakSIP+p3FVdI6Xsd+2QQq+5qokUuXYnpy9kc0Xnlpf8ed7UP4KoYH/3OsCX4q31Rzl5LrP8AzWPKzcgiEhLIM8MBn4YN/mXRCRUKZUsRt+9Z4A3Szj9BDBYRPwxqoxGAK4Zwb4EJgFzzO8rqpybRiA2MQ2lYMORFDqFBXFVl9qpabt30VaSLxpVEM38vTifmUeOvfiCIgA/HU7hw03HC9xESr6hdQ1vwuOjOldrOs+dPQO04fYreyMiXNY1CqUUaWlpJCUl4XQ6adOmDUFBQeYZVxc6Py4uDj+/ZQwY8Fih/f7+H+Ob7cvlkYH07d2aY8eOERkZic1m4/jx45w7dZwHxw+mRYsWJCcnExoayrlz55g3dSlLliyhc+eO1ZrP2uLrZWV4FQIKwIqdCcSf1wGhPqhICSEcWGy2I1iAJUqplSIyXUQeMY9ZBiwCEJHWwDtKqXFmW8NnwA7ADvyK+bSPEQiWiMgfMALHzdWWqwYqz+Hk2td/JtfhdO878MIYfL1Kr9utDmlmuwHAqO5h3NQ/ggc/2M7r3x9hwV39Cx27+Jc4Zn+1j5ZBPu4646L14QDJadn8cDC52gNCVGQkO485+fuLc/Dx9iI9Pb3YMSLC0KFDueqqq9wNw2B0M123bh2pqalEREQwe/ZsgoODmTZtGikpKezcuZM9e/awceNGNmzYwJw5c/Dy8sJisTB//nxatGgBwPTp09m1axcAzz33HJ07V28e6xuFHsdQX0hVuqTVtgEDBqiSphyuSzJy7Kw9kExaVh4BPlb3kn5AsaoLVyNi4deNffN/+I2E81nu68a0acJ1fdrw169jaRHoTWq60WTz6NUdiW4RUKzBVMqoTsDc9+fPd3Mx2463zcIz47vx7oZjxJ3J5Olx3Qht4oNS8MLK/ZzJyG8emjGyM9f0CGPsXGMg1ht39HNf76fDqXyy5QQju4Ux97Y+BPiU/rwx538HePuno+b5xT8DgMhgP3pFNKvYB2965Zu9vL7+OK8PE5wOO6mpqQQGBhIdHU1YWBgWi4Xt27ezc+dOQkJCaNeuHT4+PlgsFmw2G5GRkURFRRUKFAAXLlzgtddeY8KECfTv37+Ud9dKMu2TX9mbcIEf/u9KTyel0RKR7UqpAeUdp0cqV7M739nMzpPnq+16j17dkXnfH2FvQhp7E9IAeOHaGDqFBTLyn+uZ9/2RKl1fBHLtTp5bkd/w+rdvYosd5+9tZeGky+gb1YzvYvO7cj7y8Y5Cxz04vD0zx3QtcW75gpr42XA4FQ99uL3UY/y8rMS+MKaiWTHO8fMFYNTo0fjYSi45RUZG0rVrV3755Rf27dtHbm4uTqfT3VPGarW6g4SxrKOQmWlUedSnB6i6QjWyVcfqMx0Qqpmri5+fl5WFkwfQuqmf++kdCjytF3iSL/oUbxwPzQO88bJaePiqjpzNyEVh9JkPbWLc9LY+PZKMHHuhron5T9gld18suM8i0L5lIKfTssnIsSOAn7eV7DyHkRLJXzi8TTM/9xP/uJhwvv9TExxOVeiJ3t/bWuGF2B8Y1p6ruoSaSxSaeS7wGby/MY6PNp+o0LUKqugMn127dqVr166F9uXk5BAXF8fx48fJzc01p0B2ur9brVY6dqyfbQGepFTjWnWsPtMBoZr1jWzGwdMXycpz0KFlIGHmzbsqfL2stG5WvM9/yyAfWgZVvZdPmxKuXRaLRWjfsmqzetqsFrqVMc/8pfZeck9CdwkP8j4+PnTp0qXQQDSt6hSqwS+w01DoOQyr2exrY5g1sQdQtQFBjd2lTrvgqqnSn33d4XTquZDqC11CqEFPLN1NEz8bPjYrPjYLPjYLY2LCGdIhxNNJq/NcD5R//Tq24OKWQJFRukX2fbDpOKADQl3iVEr3MqondECoAf3bNmdgu2DOZeaSlJZNjt1Jjt1Banoux85k6oBQAV1bNSHI18anW/LbEQr2nMrfWfhHH5uFtiEB+NVwV1yt4nRorj90QKgBncOCWPLgkGL7r5//s+6lUkFjYloxJqaVp5OhVQOlSwj1hm5D0DStRikFFn2nqRf0r0nTtBrl6lqs1X26ykjTGplPt5zgy12nsFqMQXdWMcZv+HpbeWpct0p3Qy6PMXVFtV5SqyG6hKBpjcyyXxPYHX+BjBw7F7LyOJORy/YT5/h6dyJbjp2p9vdzKvTItHpClxA0zcMuZuex5dhZqjDDdKWcy8glpk0TPn0gv+PDsdQMrnplXY1U7Shzum+t7tMBQSvVlClTWLlyJaGhoezdswdObOKsTwS33j2FuLg4oqOjWbJkCc2bN2fLli088MADgHEDmDVrFtdffz0AY8aMITExEbvdzrBhw3jjjTewWnW3UJf//HiU13+o2pxUldW+ZViJ+7+LTSLhfBZOpzFlSc+IJlzdteRjK0opPdtpfaEDglaqyZMnM3XqVO65605Ycg/EfsmcnwMZceWDPPn0GubMmcOcOXN46aWXiImJYdu2bdhsNhITE+nduzcTJkzAZrOxZMkSmjRpglKKm266iaVLl3Lbbbd5Ont1RmauAz8vK0sfKt5Vuaa0axFQaLu5vxeBPjZW7k5k5e5E9/7WTX1ZNSPYXMAn/8bu2pYC30vj1JPb1Rs6IGilGj5sGHHf/AvO/gaxxvrDK3Yms+7NGwGYNGkSV155JS+99BL+/vmT2hVcdB6gSRNjziK73U5ubm6DmtfmXEYuH246jp+3lZBAb0ICfAgO8CYk0JvgAO9SZ1z95Ugq0/+7E4dTkZFjJzjAm5g2TWs59fma+Xuz49lROJwKi8W44T/7xV4+3XqSXrO+Lf8CGM0EBQOFYGxn5Tno37Z5zWZAqxY6IGglSzkEK2fArvVg9YYBf4BtC0nK9iK8gzFXU3h4OMnJ+Sufbt68mSlTpnD8+HE++OCDQmsKjB49mi1btjB27FhuuummWs9OTVl7IJlX1xwq9fUgHxvBZnAICfChRaA3rZv5EXcmg5SLOdzUPwIfm4XLooNrMdUl87YV7mPy8JUd6RQWZM54a6xZ4VT504K4qpWc7ll2Cx/nmn3X6VRcWUsr+2lVowOCVtyvHxnBwMsXrn4OVrwC2xZC1FCwbiz1tEGDBrFv3z5iY2OZNGkSY8eOxdfXmO119erVZGdnc+edd/L9998zatSo2spNjXLdHL9+9Hf4eVk5m5FLanouZzNyOZuRU+DnXOLPZbLz5HlS03Pc5z87vjtN/b08lfwyRYX484fftfN0MrRapANCI1OooXjvXgDOnj3LrbfeajQUB+ax5JqzNO9+Bblj/skT147iyMlkei8OYe47MwgLO0piYiLr169n9uzZZGZmMnPmTF5++WX3e3Tr1o2AgAD27t3LgAH5izT5+voyceJEVqxY0WACgqvyq4mvF5HB/rSvwPLDWbkOdsefJzPPUWeDgdY46XEIjczkyZNZtWpVoX1z5sxhxOWXcfjJ9owIOc2cYz3gugW8/dh4SEugY1Qr1mzax59m/oUJEyYwf/58nnjiCW644QamT59OUlISH374IXa7HYDjx49z8OBBoqOjSU9PJzHRaKS02+188803xRamqc9cjayVmV3Vz9vKoPYhXKWrUbQ6RpcQalFdaEodPnw4cXFxhfat+OxT1t1phdPZTHrq31x5/9946Z1R/Ovbo6Tk+pCRnUq//v0JCgpi9OjRPPXUU6SmprJx40aWLl3K119/zaJFi3jxxReLLTqflJTExIkTycnJweFwcPXVV/PQQw95JvM1oCoL8mhaXaMDQgNRUlXQ0qVLmTVrFrGxsWzevJnOp07RZOJEAFJSUugYGYbVmUN8ygXCQ/vBrR/y+rPPcfjocQKfsvDPvz7Dmi37+eSTTzh58iR9+/YlLS2Nb7/9lp49e7Jw4UKaNGnCF198gb+/P/v27SuWrrCwMLZu3Vqrn0Vtci/Z6eF0aFp10FVGDURJVUExMTEsW7aM4cOHk7pgAaf+/CQHevXm4L59XLhwgX2Tslh1k4NsOzimrIEDK5ng+IYgPyt4+TJlxrNEREQwYMAAHnvsMYYOHYrNZqN58+YsWLCAW2+9lWHDhhEdHV2oR1Fj4ioh/O3rWH4+kkqu3enZBGlaFTTOv+IGqKSqoG7dugHgTE8nY9NmY2deHssem0HTpk3x+d0U2m2aj69N+GbW9UwI2EnbwdfRav1WEk6dwmaz8dprr7mvN3ToUDp16gTAhAkTmDBhAgBvvfVWox153KVVEC2DfPguNonvYpMIa+LD6B6tuKJzS0Z0q9oIX02rbTogNBBKKfauW0Naagpxu3+lbc8+iAiO8+fJOXQYa/duRP7jH5y8/wFOJydh8/eHMS9C37tp/59+fLrqFya88SKLf87j2uvaMH/+fDIzM1FKERAQwJo1a7DZbHTv3h2A5ORkQkNDOXfuHPPnz2fJkiUe/gSqJsfuYP2hVDJy7OTYHdgLTCzkmt/HVRpwtQW5tv/vms5k5zn54WAy6w6m8P7G47y/8TizJnRn8uW626ZWf5QbEETEF1gP+JjHf6aUel5EegNvAoFAHHCnUiqtyLldgP8W2NUeeE4p9S8RmQXcD6SYrz2llPqmatlpnJwOB1//+xU2fbeavOwsPv/bs/QdM4Gr732Q5H/9C2XPo+mECZx6YiZPpKbwfU4OOenpREREMHv2bHpfMYFdO3fS6Q//ISoqiqVLlzJ//nySk5MZPXo0FouFNm3a8MEHH7jfc/r06ezatQuA5557js6dO3sq+9Xiq12J/N/SXdV6zY1Hz+iAoNUrFSkh5ABXK6XSRcQL2CAi/wP+DfyfUupHEZkCPAE8W/BEpdRBoA+AiFiBBGB5gUNeU0q9UvVsNA55uTnMu9uYNqJJyzCUcuLfpCkR3WI4tPEn+o6ZgNfWPQA0DTWWn0xf+z0AZ95+m3BfP5bt2MGrH30EwF/+8hcAlixZwluL3mfIkMJz6URHR3Pw4MES0/LJJ59UfwY9KC0rD4AVj1xOiyAfbBajXOAqJ7h6ESlzT/626/XC+wHCmvjWbKI1rZqVGxCU8T893dz0Mr8U0AWj5ACwBlhNkYBQxAjgN6XU8UtObSOXVmCaiIhuPdi//nsupqaQdPQIwW0iidv9K3nZOYy6fyq9Ro4xjluwALl8KIiF6M8+wzsqiokTJ3LHHXfw+OOPc+rUKQ4fPszAgQM9la06IddhNAZ3DgvCz7txtodoWoV6GYmIVUR2AsnAGqXUZmAvMNE85GYgspzL3AYUfaycKiK7ReRdESlx9isReUBEtonItpSUlJIOaTT8zEniht58J2MfeZyHF37CqPuncuU99zPvsy954cOlpGZmMm7yfSxcuJDly5fTacxodmZnMzUjnev+9DgAPXr04JZbbqF79+6MGTOm0HTUM2fOJCIigszMTCIiIpg1a5anslurcvKMgFB0Ph9Na0xEVWJEjYg0w6jymQbYgXlACPAl8KhSKqSU87yBU0APpVSSuS8MSMUobbwAhCulppT1/gMGDFDbtm2rcHrrmhvm/0yAj40P/jDoks7PTk/njT/cRrOwcMLad8Q3MIgRUx4i4cB+/jv7Sa5/8nna972smlPdcGXlOnjl24Nk5trZHX+Bg6cvcuTv4zydLE2rdiKyXSk1oLzjKtXLSCl1XkTWAWPMuv9rzDfrDIwv49SxwA5XMDCv5f5ZRN4GVlYmLY2S2avlfFIi55OM6SD2rfuO9v2MIBDYvMR4rJViT8IFFm44BkCLQB+Gdmzh4RRpmmdVpJdRSyDPDAZ+wEjgJREJVUoli4gFeAajx1FpbqdIdZGIhCulXCtxXI9RBdXgVWWKAx//AIbcdAcZ58/S44qRxG74gZ2rv+bQ5p+B/ColrWJc8w99fN8gHQw0jYqVEMKBxWYvIQuwRCm1UkSmi8gj5jHLgEUAItIaeEcpNc7c9gdGAQ8Wue7LItIHo8ooroTXG5yqLgwjIgy9+Q73duvOXek0cChLX3gaAP8mnltgpT5yB+e6MMmUptUBFelltBvoW8L+ucDcEvafAsYV2M7EaGcoetzdlU2sVlxUTG9u/MtslFJYbfV7KuW5c+fy9ttvo5Ti/vvv57HHHuOJJ57gq6++wtvbmw4dOrBo0SKaNWsGwO7du3nwwQdJS0vDYrGwdetW9/oLFeHqQloTC8trWn2ku1Q0ANF9+tOub7ntRXXa3r17efvtt9myZQu7du1i5cqVHD58mFGjRrF37152795N586defHFFwFjKu277rqLN998k3379rFu3Tq8vCoZEM0SQgNa0VPTqkQHhEZsypQphIaGEhMT49539uxZRo0aRadOnRg1ahTnzp1zv7Z7926GDBlCjx496NmzJ9nZ2YAxSK1nz5706tWLMWPGkJqaWum0xMbGMnjwYPz9/bHZbFxxxRUsX76ca665xj1x3uDBg4mPjwfg22+/pVevXvTu3RuAkJCQSs+npGuMNK0wHRAasVIXyxkxgsOHDzNixAjmzJkDlP5EbrfbmT59Oj/88AO7d++mV69evP7665VOS0xMDOvXr+fMmTNkZmbyzTffcPLkyULHvPvuu4wdOxaAQ4cOISKMHj2afv36FVqxraKUu4SgQ4KmgQ4Iteanwymczcj1dDIKGT58OMHBhRd3X7FiBZMmTQJg0qRJfPHFF0DpT+RKKZRSZGRkoJQiLS2N1q1bl/veOXYH+05dIC3bmDKiW7du/PnPf2bUqFGMGTOG3r17F5pS+29/+xs2m40777wTMALUhg0b+Oijj9iwYQPLly9n7dq1lcq/uw1BxwNNA/Rsp7Xi+JkM7l64BYBu4UEeSUPPxT3pFtyNKyKvwCpWbBYbIb4hxFhjCh2XlJREeHg4AOHh4SSb02UUfCJPSUnhtttuY+bMmXh5ebFgwQJ69uxJQEAAnTp14o033ig3PX//OpbFG4/jbbNweYcQfL2sDOp6NW8vv4GebZoy67lniIiIAGDx4sWsXLmStWvXup/mIyIiuOKKK2jRwuguOm7cOHbs2MGIESMq/Jm4SwgVPkPTGjYdEGpBgZmUcXpw/ZTYs7HEno0ttO/WlrdW6FzXE/nWrVvx9/dnxIgR9O/fn+HDh7NgwQJ+/fVX2rdvz7Rp03jxxRd55plnyrzeeXMyuTsGRvHLb6kcSkpn5eYDWAOaMSLSws/LlrFx40ZWrVrFSy+9xI8//oi/v7/7/NGjR/Pyyy+TmZmJt7c3P/74IzNmzKjU5+FuQ9ARQdMAHRBqRXSIP/Pv7MfDH+0gI9fukTTYLDYmdZ/E9H7TcSgHDuXguZ+f4+OtH5PnyHMfFxYWRmJiIuHh4SQmJhIaaiwEX9oTeRNzMFyHDh0AuOWWW9ztDmVxKmjXIoBZE3sY205Fj/6D+O3kaX4ICeLt+W/QvHlzpk6dSk5ODqNGjQKMhuU333yT5s2b8/jjj3PZZZchIowbN47x48saLF+c3eGKzjoiaBrogFArRIRxPcPpF9XM4+kQEWxiw4aNpwY9xY+7f2R/5n4y8zLx9/Jn4sSJLF68mCeffJLFixdz7bXXAqU/kbdp04b9+/eTkpJCy5YtWbNmjXultvIcS83gmS/2uLfb3P0PspLT+fD+QQztYASeI0eOlHr+XXfdxV133XVJn0XyxWz+/Lnx3pHN/S7pGprW0OiA0FiUMGXGQ5Mf4sj3R7h45iItW7fkhdkv8OSTT3LLLbewcOFC92I5QJlP5M8//zzDhw/Hy8uLtm3b8t5775WbnL6RzfjpcAr/23Pavc+hFG1D/GkbElAtWS5LwrksUtNzgMJVeprWmOmA0EjYlZ13975L5+adCfQKxCpWpv9zOo/L46w8upJlh5exOmA1j4c8Xqy3zpQpU1i5ciWhoaHs27cPgKVLl9KjRw9iY2PZsmULsbH5bRMvvvgiCxcuxGq1Mm/ePEaPHg3A9u3bmTx5MllZWYwbN45f5871WJfPvlHN+fi+QTzwwXYmL9rCe/cOJOViDgqFRYS2If4E+dbvkd+aVlk6IDQS/jZ/Mu2ZzFw/s9RjMuwZJe6fPHkyU6dO5Z577nHvi4mJYdmyZTz4YOEpqPbv38+nn37Kvn37OHXqFCNHjuTQoUNYrVb++Mc/8tZbbzF48GDGjRvHqlWr3OMKPGFoxxbMv7MfkxZtYfCLhYPgZdHNWfrQUA+lTNM8QweERuLrG77m2IVj+Nn8cCgHTuXE4XSgUMa200mrwFYlnjt8+HDi4uIK7SutnWDFihXcdttt+Pj40K5dOzp27MiWLVuIjo4mLS3NvUznPffcwxdffOHRgAAwvHNL3rijHxuOpDK8U0tsFuHNH3+rc2NGNK026IDQSLTwa0ELv5qf4jkhIYHBgwe7tyMiIkhISMDLy8s9rqDg/rpgXM9wxvUMd29/tfuUu31B0xoTPVJZq1YlrcAnIqXur4usFsGuW5q1RkiXELRi0lKzOLH/LABWmxDVo+IrsUVERBSagyg+Pp7WrVsTERHhnpiu4P66yGYRktNyWLM/CQH6tW1OcIC3p5OlaTVOlxC0YrZ8dYwfPz7Ijx8f5Pv3D7Dzu5Pln2SaOHEin376KTk5ORw7dozDhw8zcOBAwsPDCQoKYtOmTSileP/9991jHOqaAW2DyXU4uf/9bdz3/jZe+t8BTydJ02qFDgi1qL5UQuRk2fHysXL3X40G4Of++RhDhgzh4MGDREREsHDhQpYvX05ERAQbN25k/Pjx7q6lPXr04JZbbqF79+6MGTOGN954wz0t9YIFC7jvvvvo2LEjHTp08HiDcmluuSySn2ZexcppvwMgK8/h4RRpWu2Qkup266oBAwaobdu2eToZl+z6+T8T6GPjgz8M8nRSyvTGQ98X2u49IpLf3dzJQ6nxrCv+8QN9Ipsx97ZiiwZqWr0hItuVUuWuoqXbELRiOl0WRurJi3QZbHRD7TQgzMMp8hyl9ExHWuOhA4JWzDV/6OHpJNQZCsWZjFyWbD3Jb6npTB4aTXhTPfeR1jDpgKBpZbBZLPx0OJWfDhvLgrYNDuCOQVEeTpWm1QwdEDStDC/e0JPEC1m0CPTh7oVbsFl1BZLWcOmAoGllGNzeGINxNCUdAG+r7pinNVz6f7emVUCew+iN56UDgtaAlfu/W0R8RWSLiOwSkX0iMtvc31tENorIHhH5SkSalHBuFxHZWeArTUQeM18LFpE1InLY/N682nOnadXgg41xPPzRdgBdZaQ1aBV53MkBrlZK9Qb6AGNEZDDwDvCkUqonsBx4ouiJSqmDSqk+Sqk+QH8g0zwW4ElgrVKqE7DW3Na0OufZFfv4LSWDoR1C6Beln1u0hqvcgKAM6eaml/mlgC7AenP/GuDGci41AvhNKXXc3L4WWGz+vBi4ruLJ1hqc9f+Ad8fCtkXgdJZ/fC3599rDgNF28NyE7rQM8vFwijSt5lSoQlRErCKyE0gG1iilNgN7gYnmITcDkeVc5jbgkwLbYUqpRADze2gp7/2AiGwTkW0pKSkVSa5WH33/V6b8ey2hV/yBmO6d3btdK7NZLBaKjlJ/8cUX6dixI126dGH16tUAJJ29wOVXjaJr16706NGDJ5+sXMFzT/wFvtx1ioTzWQB8suUEALkOJ2P+9RM7T56vQiY1rW6rUEBQSjnMap8IYKCIxABTgEdEZDsQBJS6ooiIeGMEj6WVTaBS6i2l1ACl1ICWLVtW9nStvmg3nMl9vFh1lz8Fxwa7VmYbPnx4ocMLrsy2atUqHn74YRwOB0u3neS3Vlfje8c8/vDqEn7++We+Wvk1GTl2zmXkciEzjwtZeaRl55GeYycjx05WroPsPAfJF7OZ+MYGHv3kVy6f8z05dgfWIm0Ge+LP18KHoWmeUalup0qp8yKyDhijlHoFuAZARDoD48s4dSywQymVVGBfkoiEK6USRSQco/TRoOnmyDLc9jHDr44l7vQ5+PlP7t2VXZnNxzcM37a9OJ+Zx7x1cVzIDmby66sI2lD5JHV5ZlWxfVaL7mWkNVzlBgQRaQnkmcHADxgJvCQioUqpZBGxAM8Ab5ZxmdspXF0E8CUwCZhjfl9xKRmob+rRXIK1yycIIgeCI65Ch5e2MtvEa/ryzoajJKXl4MxOJ/O3Ldx59/1sTrXSvmUg1/dtg9P8JSgFTqVQmN+VsRZCn8hmbI07i4ggAhYRBPC2WRhfYGU1TWtoKlJCCAcWi4gVo4ppiVJqpYhMF5FHzGOWAYsARKQ18I5Sapy57Q+MAh4sct05wBIR+QNwAqMdokETEVS9mQS7Zp35KJasPan49QgxPhHzYzmfllihecJLW4HtTHouNosF5XSQ8uU/6HnNbfi3aM3oMAu3XhbJoPYVW+ynosdpWkNSbkBQSu0Gis39q5SaC8wtYf8pYFyB7Uyg2F+XUuoMRs+jRkNXGeXL2mPMDZSXajTeigh5pzPIunAG5Si/l1FpK7N9fyCJhPNZnFn1b7yCW5MaPZL9p9I4cTYThb7Ra1pZdIVoLdNVRgb/fqFYm/nQakZ/Ws3oT9hj/Qi5p7vxYgU+o9JWZgM4t/4D+oR503zE/QC8O/ky2jT3c1cVaZpWMj2XUS0S0QHBTQRnph1Hen7ntHtm3Mf6n9dzNieNiIgIZs+eTXBwMNOmTSMlJYXx48fTp08fVq9eXWhlNpvN5l6ZzX4xlbSN/2VzcASyezoWEdZ0eBKho/7sNa0cesW0WnTzm79gs1j45IHB5R/cwJ37/DAZW0+X+Froo33xbh1Y6WsqpUjPsXPibCYOp/H/OirYn2b+3lz1yjpi2jTl37frlc+0xkevmFYHCbpR2SXoqki8wgOMDcGoJhKw+NrwahVwSdf8cPMJnv1iL6/c3Jub+kcUes0isGb/aYa8uBarRfjb9T25orMe16JpBemAUJt0lZGbLdiXwKGtq/WaaVl5APzf0l38vlc4vl5W92vTru7EL7+l4nDC5zvi2X3yvA4ImlaEDgg17FDSRd788Teych0cSrpIl7AgTyepwWrXIr9kkZ3nKBQQWgT60LqZH2ZNki6naVoJdECoYf/bc5plOxLoGBpIRHM/xsS08nSSGhylFD8eSiH+XKZ7nxTp5PvCyv0cTLoIGNVHbUP8azWNmlYf6IBQA06dz+Kdn47xyFUdcJh1RN8+NhyLpWZGItgdTrYfP4fdqRABqwgBPjZ6tG6CSMMf/XAoKZ3Ji7YW2hd7Oo1m/l7YLILNYiErz8G4nq14/fZ+ADX2u9C0+kwHhBqw4Ugq7/58jHd/PoZFwM/LSk3el7/Ze5pHP/m12P6P7x/E0A4tau6N64j2LQMI8rFxMcfu3nfbW5uKHTegbXMdCDStDDog1ACLeffvFdGUK7uEMrF36xp9Unc1pv7n7v408fXi5LlMZn62mzve3szDV3Zg5piuNfbedYGX1cL2Z0fx3i/HeGnVQRxOxVt398fuVMaXw4ndqRjaQY9S1rSy6IBQg85m5PLlzgRW7Exw9y669/Jo7r28XbW+j2ssSb+o5rQM8mGQM5isXAf//v4wR5LTyzm7YfC2WXhgeAdSLubw7s9xXNPj0ttqfjiYzMebjXUQbBbhrsFtubxjwy9paZqeuqIGDO0Qwo39Iugb1ZxeEc3oE9mMflHNOJ+Zyy+/nan297OZC7/bzZXGLBZh0tBoWgQ2vtW9/LysVZ6iYtmOBH48mMLJs5lsO36OSe9uYfmv8dWUQk2ru3QJoQa0bubHq7f0LrZ/3NyfamQcgqtaXI9xAESq9Dk88P42vt2fRIeWAax6bDhp2Xk89MF2Zvx3F0lpOTw4vH2jaKjXGiddQqig/afS+OvK/Xy2PZ7EC1lMXrSF+xZvY9XexBKnYi6JxVLytM1V5bqkvk8VDI6X9jk39fMCYES3MACa+Hqx6N7L+H2vcOb87wA/H6n+Ep6m1RW6hFBBX+xM4J0Nx4rt/y42iUev7uhuFyh4Gyp6U3I4qZEZN11XLNr3vmh6svMc7jl+XMHDdU7RYFLwddfPVhGP9dLZnrSdhPQEJnaYWOZxrgZ9pwLrJST1uQndWbX3NCkXc9z7fGxWpo/oxMrdiZzLLHWlWE2r93RAqCC7Q+FlFZ6f0IPktGwycx08ObYrHZ/+H/O+P8K8749U6DptmoVVe9pcQaboTT3uTAa+Xla+2ZPIf7ee5KfDKe6RupfC22bh62m/o1Mtj7ZOy01j8qrJAIxtNxYvi1epx1rNgNXtuVVYRbBaxF1qGNEtjI6hgfxj9UFzjIKFVk19eH5CDy6LDgYgyNeL7q2bcOp8VqHr5pprNHhZdaFaa7h0QKggp1L4eVm5a3DbQvs/+MNAfivSk6doHXPBzZoYF+C6yVsKvNHhpItk5znJczh5+KMdhDf15f7h7QkJ8HZXMbliQ/62KrRt/Gxs/HriPGsPJDNl8VaCfLxo3cyPN+/q527QrkmBXoEMDh9Mx2YdywwGANf3bUN2noNchxOnU7lLZTtOnGP5rwnu485n5nH7wCh+OpzCLf/ZyAPD2jNjVGd8vazkOZwE+BT+08hzGJ+Dt03Xy2kNlw4IFeRwKvfTZ0HDOrVkWCfPTpLmumm70ud0Kp75Yi9BPjaa+3vTzN+LDX++usT0V1Rmrp1nv9jHxew8jp/J5LvYJC5k5RFSCz2ZLGLh7WvertCxrZv58adrupT42tJtJ3nis90AdG0VxN+uiyEzz8Hfv4nlP+uP8sPBZIZ2aMHu+Atc1TW00Ll5ZgnB22otdl1Nayh0QKggh1LYHYrNR88wsF1wnepp4moXcN3vP9pygs3HzvLSjT159dtDDO/UskrBAMDf2+buOfX+xjieW7Gv3k0Qd/OASIJ8bRxJTuf+4e2xWIRAHxt/v74n13QPY9aX+1i2I56wJr7cM6RwSTDX7qoyqju/d02rbjogVFCQrzE1wq1vbeLREZ2YMbKTx4JCclo2exIuAEZ11PcHks2fhfhzmcz5JpbIYD/Cm/qRfDGn2htCXbnek3CBZn5e3PPuFjJy7HhZLTw6ohOPXNWxWt+vOo2JCS9x/5VdQln3RGiJr0F+G8LptGwOJV10V6vl2B2cvpCNn7eVyzu00FNjaPWaDggV9H/XdOHa3m2Yv+4I89YeZt7aw1wW3ZyDpy/SN6o5f70uhsjg2plB86nle/kuNqnQPm+bBR+bhRUHksnIdZBxNot73t0CQFq2vaTLXDJX/fq9BSaUi2jux8VsO/sT06r1veoKqxn8p3+6s9Rjwpr40CeyGT3bNGVMTCs6huqpzrX6RQeECvKyWujeugnzbutLnsPJuoMpgHGz/fFQCnP+d4A37uxXK2nJzLXTLbwJc27oCRiNwy0CvfH1snLXoLb0jmiG3enEqYzqpI6hlV+OsiwTeremVVNfcuxOlFJYRBjSIYSx//qphI6vDcOQDiG8fc8AsvMciOR3x/W2WvD3sZJwLosNR1LZE3+B1fuSWL0via+m/c7Tyda0StFrKleD4S//wImzmUQF+/Ofu/vTLbxJtVz35VUHmL/uN3y9LAR427BYBJtFSLyQzcDoYJY8NKRa3qe6XPmPH4g7k0mviKacy8ylXYtAFk2+rMrtF/XF2YxcfvktlakfGzPPvnRjT269LMrDqdI0vaZyrXpsZCe+i03imz2nOZR0sdoCQoeWxpN9aJAvwzq1wGk2bDucilHdq388Q1VNHhrNhiNncDid7I6/wMmzWZzJyCE0yNfTSatRh5Iu8o/VB/kuNqnQqHFP9z7TtMoqNyCIiC+wHvAxj/9MKfW8iPQG3gQCgTjgTqVUsQpkEWkGvAPEYNRuTFFKbRSRWcD9QIp56FNKqW+qmiFPuKFfBJ3Dgvhmz+lir53NyGVPwgWGtA/B21bxPvt74i+w5dhZAJ4e343RVZi9s7ZMvrwdky9vR47dQZdnVmGzCFuOnUWpgmMeCo91UCjjdXM7tImPR26kZzNy+eFAMk6l6BnRlK6tyg/qWbkO5n1/mLfXH8Xf28rDV3ZgRLcwerVpWivjMzStulWkhJADXK2UShcRL2CDiPwP+Dfwf0qpH0VkCvAE8GwJ588FVimlbhIRb6Bgy+trSqlXqpgHj1NKMed/BwBjcFiO3cHqfUmEBfkw7/vD7vlvru/bhqfHd6vQLKRvrv+Nr3cnEuRjIzokoNzj65K0LKMR2+5U7uqTytjx7CiCA7yrO1llenfDMV7/wRht7mOz8PH9g+jfNrjU4384mMxzK/Zy8mwWN/aL4KlxXWtlTIam1aRyA4IyHulcQ3G9zC8FdMEoOQCsAVZTJCCISBNgODDZvFYu0KAmg7E7nPz161g2HEllQu/WDGofzPh5G0pch2D5rwn0jWrGPUOiy72uw6HoEhbE6hnDayDVNatlkA+b/jKCtOw8dyOz0UlHCsyRZHSTFfJHcn+16xSvfHvIPQisNmXnOfDzsrJi6uXcvXAz89YeYfGUgcWOS0rL5v99tZ+v9yTSvmUAn9w/mCF64R2tgahQG4KIWIHtQEfgDaXUZhHZC0wEVgA3A5ElnNoeo0pokVnFtB2YrpTKMF+fKiL3ANuAPymlzpXw3g8ADwBERdW9BrrdCRd475c4wLihfbXrVInH/a5jC24eEOGeRbM8ClWvZy9t1dSXVk0r13YQHOC5J2ynMhbD6RwWRGRzf/faEi4Op+LDTcd5ZfVBchxO/jSqMw9c0R4fmx65rDUcFQoISikH0MdsD1guIjHAFGCeiDwHfEnJT/42oB8wzQwic4EnMUoSC4AXMEobLwCvmtcs+t5vAW+B0cuoUrmrBT3bNOWF62LIszvdvWme/3Ifzfy9eO733bFaBB+bhSs6h+LnXfGbRz3q/NUg2J1OrOYoZLtT4W/JbwPYm3CBp5fvYVf8BYZ1asEL18YQ3aJ+VeNpWkVUqpeRUuq8iKwDxph1/9cAiEhnYHwJp8QD8Uqpzeb2ZxgBAaWUe2SViLwNrKx06j3M4VTM/mofJ88aM2O67uHBAd5EBvtzQ7+IS752Y44HT3y2m7AgHxT5gfHqrqGM71XyKOPqkOdw4m02BGfm2tl58jx/WrKL7cfPEncmkxaBPsy9rU+Nr4+taZ5UkV5GLYE8Mxj4ASOBl0QkVCmVLCIW4BmMHkeFKKVOi8hJEemilDoIjAD2m9cNV0olmodeD+ytpjzVijPpOUx5byu74i8Q6GOjgzn4S4CoYH9+X8Wbl5dVOJR0kbMZubXewOop3cKDiAz240BiGoeTxN3OkJ3n4PMd8ew91YEnrulSI9ND5Nid7qmtXUHo+wNJDGoXwnV923Dv0HY09S97plVNq+8qUkIIBxab7QgWYIlSaqWITBeRR8xjlgGLAESkNfCOUmqc+do04COzh9FR4F5z/8si0gfjYTgOeLAa8lNrZn+1n/2Jabx2a2+u69Om2p8agwO8cSro98IabuofwR2DougX1bxa36Ou6RvVnJ9mXl1sf57DyXMr9rJg3W8cTUnntVv74O9dvUNo8hwKH7Nb8Gu39iEtK4/B7UP03ERao1KRXka7gb4l7J+L0aW06P5TwLgC2zuBYiPklFJ3VzKtdcZvKel8uesU067uyPV9L71aqCxPjO5KZo6DjUfP8PmOeLxtlgYfEErjZbXw9+t70qFlIH/7Jpbb3trEO5MGVNuAN4dTcej0RXcbUEybptVyXU2rb/TomUuwfEcCInDrZSV1rKoeTf28+Oetfdj4lxGEBHg32DmCKkpEuG9Ye966ewCHk9K5/o1fOHj6YpWvu+noGcbP+4mDSRcZ1L70cQea1hjoqSuK2JtwgfsWbyMz15jOWUTMycxgXM9wZk3swc+/paIUjJ37Ezf2i2DWxB4lXivhfBZ3vbOZC1l52B1OHE7Fw1d1rND00O9vjGPBut/w97ZyNiO3XndBrU6juoex5MEhTHpvC6Pnb2D+I4MZF3ZpJaeVu08x9eNfadPMj/l39mNsTN0fDa5pNUmXEIo4lHSR02nZ9I5sxpiYVlzTI4yR3ULxtll475c4nl6+x71YysVsozdKaX49cY5jqRn8rmMLbugXgdUixFZweug1+5NIvJBNl1ZBjIlpxbV92lRH9hqEnhFNGdovHHKdLDye4t4/ZcoUQkNDiYmJce9bunQpPXr0wGKxUHBixLy8PJ5//GFOvfsIZ957hF0r33O3A23fvp2ePXvSsWNHHn30Ufd0G5rW0DWKEkJyWjYfbj6BvcgI2JL+zJfvMNbd/et1MbQtMGXE/y3dxWfb4/l060n3ovYAvSJKr292LdT+1+tjaOLrxfrDKaUeW5TDqRjQtjnz7+xf4XMaE9dcQfFZ+cNfJk+ezNSpU7nnnnvc+2JiYli2bBkPPli4z8LSpUux5+US/cACdjx1Bd27d+f2228nOjqaP/7xj7z11lsMHjyYcePGsWrVKsaOHVs7GdM0D2oUAeHLXaeYt/YwNouUWPUiBWrocx1Owpv6FpuX5tnfd+eWAZH0imiKCNgdqthC7EW54k/82Sy6t/bCIlIomJTFqRTpOXa2Hz+Hv7eVLmFBusdLAQHmCOHjmTnsT8+ie6Afw4cPJy4urtBx3bp1K/F8ESE3OwtRDrKysvD29qZJkyYkJiaSlpbGkCHG1OL33HMPX3zxhQ4IWqPQKAJCVq4DgNgXxrj7mleG3WGMQu4d2dQ9VUE5sQCAmwdEsOjnYzz80XaWP3w5DqfifGYe24+fw8sqeFkteFmF5v7exQJQgLeNTUfPcuOCXwBjRPTkodEM7hBCm2Z+lc5DQ+MaSCxK8XXKeboHVu4zuemmm/j7gg+I/dedRP3LzmuvvUZwcDDbtm0jIiK/51hERAQJCQnVmXRNq7MaRUBwTZZmu8Qn7GteW8/RVGP6pR+fuLJQVVJZWgT68Mad/bjznc1cN/9njp/J5FhqBr+YN3kXm0XY+JcRtAzKDwr/vKUP+xPTyLY7WH8ohW/2JPKnpbsA+Oi+QVzescUl5aWhUK6inhO8pfJBfsuWLYjFQtcZH/P9o5cxbNgwRo4cWWJ7gR6ZrDUWjaJROcfhxNtmuaQ/7ITzWe5gALgnsquoy6KD+ei+Qe7Rr7cMiGDxlIG8c88A3ryrH/cMaYvdaVQPFdTU34shHUK4qksoz0/owaa/jGDGyM4AxY5tjNyrsClFt0BjPEJqrp0D6VkVmvbj448/pkPfy7HYbISGhnL55Ze7Swfx8fHu4+Lj42ndunUN5EDT6p5GUULItTvJtTvZFneWvlHNy1zS0e5wsiv+PDl5xprEkxdtKfT6op/jGNQumDExFZ+a4rLoYL6a+juGzFmLzWrhis75C8Bk5Tl4f+NxrnplHWAEjLgzmWw5dharRWgb4k+LQB9SL+a4A1NjmcqiLO62GIHHD5wk0JZAXFYujtOnsOSWHzCjoqLY8O1GLMP7kZGRwaZNm3jssccIDw8nKCiITZs2MWjQIN5//32mTZtWw7nRtLqhUQQEV73/TW9u5M27+jOmjP7mX+9JZPqnO0t8bXyvcBLOZTHjv7uICg6ge+uKLZWZlevgD4u3kpnr4LLown3mh3ZowchuYaw9YCy/uGSb8XQ6uH0wF7PtRAX7cyY9l/YtA7h7SFu6tmrCgLaNc8RyQU6nqxwgKKCjvy95f3+avb/8TO75c7SOiOCF2bMJDg5m2rRppKSkMH78ePr06cPq1at55JFHWLj8O+LnP8hl//Xn3nvvpVevXgAsWLCAyZMnk5WVxdixY3WDstZoSH3qYz1gwABVsC95RWXlOlgTm8Sjn/zK7QOjCt2U8xdsMX746XAqn++IZ+5tfZj52W6a+Xvxz1v60CkskJAAH86k5zDqtfVcyMrjmfHduG9Y+3Lf/4cDydz73lau6tKSl27qVeaUC7GJadgdxjKOWume/P4gn357hN9f3Y7Owf4oZfwuk3LsLEpIJdrXm792bpO/VGeB/+auHxesO0JGjqNeLkKkaZUhItuVUsWmECqqUZQQ/Lyt/K5jC7yswidbTvDJlhNlHh/gbS11MFhoE1/+eGUH5vzvAH/9OpajqRnc6u6OWnJVlGuWzB8OpvDWj0d55vfdS33vbuEVK3U0dgE+Rqlv5ffHir3mDZwCpvySWOy1ooZ1atyN85pWUKMICGDUu29+aiQXs/OA4gvQFNxs5udV5kpYDw5vT0RzP1bsPMWyHfF8vPkEXcKCuHlABNf3bVOsC2kzPy/m3d6XRz/5Fbuz/pTI6rK/DG5P95ZBZOcZXYoR43dqEeFwZjZdA3zpEuhXZAnP/JKga7ttiD+aphkaRZVRTUrLzmPlrkSWbDvJzpPn8bZa+M/d/bmqaygAcakZXGk2GAP88coO/HlMVw+lVtO0xkhXGVXCj4dS2H7cWM654KLvABey8jibkUt5D/aRwf5k5zk4cPoiK3YmuANCtt14gg1r4sNT47oxvFPLsi6jaZrmMTogALO/2sfRlIwSXwv0sRES6I21gmMY2rUIKDRo7LNt8dgswhePXE54Uz3CWNO0uksHBIyRzDf0bcM/b+0DgFIKpYx2hbLGLFTEoeR0urduooOBpml1ng4IgNNpTDc94tV1RuOyym9kVkoV+BkUquSujOZx7tfM45Iv5hDTRvcc0jSt7tMBAbh/WDu2xhltCAjuxd3NzQI9VIz97jKDGL1WCr9euCeLCAzT7QaaptUDOiAAky9vx+TL23k6GZqmaR7VKCa30zRN08qnA4KmaZoG6ICgaZqmmXRA0DRN04AKBAQR8RWRLSKyS0T2ichsc39vEdkoIntE5CsRKbFvpYg0E5HPROSAiMSKyBBzf7CIrBGRw+Z3PaezpmmaB1WkhJADXK2U6g30AcaIyGDgHeBJpVRPYDnwRCnnzwVWKaW6Ar2BWHP/k8BapVQnYK25rWmapnlIuQFBGdLNTS/zSwFdgPXm/jXAjUXPNUsNw4GF5rVylVLnzZevBRabPy8GrrukHGiapmnVokJtCCJiFZGdQDKwRim1GdgLTDQPuRmILOHU9kAKsEhEfhWRd0TEtUJ9mFIqEcD8HlrKez8gIttEZFtKSkpF86VpmqZVUqWmvxaRZhjVQ9MAOzAPCAG+BB5VSoUUOX4AsAm4XCm1WUTmAmlKqWdF5LxSqlmBY88ppcpsRxCRFOB4hRNcMS2A1Gq+Zl3WmPLbmPIKOr8NWVXz2lYpVe6UCZUaqayUOi8i64AxSqlXgGsARKQzML6EU+KBeLNEAfAZ+W0FSSISrpRKFJFwjNJHee9f7XNAiMi2iswT3lA0pvw2pryCzm9DVlt5rUgvo5ZmyQAR8QNGAgdEJNTcZwGeAd4seq5S6jRwUkS6mLtGAPvNn78EJpk/TwJWXHo2NE3TtKqqSBtCOPCDiOwGtmK0IawEbheRQ8ABjCVsFwGISGsR+abA+dOAj8zz+wB/N/fPAUaJyGFglLmtaZqmeUi5VUZKqd1A3xL2z8XoUlp0/ylgXIHtnUCxoo5S6gxGicHT3vJ0AmpZY8pvY8or6Pw2ZLWS13q1prKmaZpWc/TUFZqmaRqgA4KmaZpmarABQURuNudecprjIQq+9hcROSIiB0VkdIH968x9O82vYoPlRCRERH4QkXQReb028lKemsprWed70qXkt8DrX4rI3lKu6y0ii8z5uXaJyJU1k4PKqcH8eonIYjO/sSLyl5rKQ0XVYF7vLPB/fad5/T41lI0Kq6n8mq/3EmO+uX3m79i33AQZC8o3vC+gG8b0GuuAAQX2dwd2AT5AO+A3wGq+VujYUq4bAPwOeAh43dP5rOG8lnp+fcuv+foNwMfA3lKu+wiwyPw5FNgOWBpwfu8APjV/9gfigOiGmNci79ETOOrp32sN/25twG6gt7kdUpG/3QZbQlBKxSqlDpbw0rUYfwQ5SqljwBFgYCWum6GU2gBkV1NSq6ym8loN59eIS8mviAQCjwN/LePS3TEmWkQplQycp4QecrWtBvOrgAARsQF+QC6QVq2Jr6QazGtBtwOfVEd6q6oG83sNsFsptct8nzNKKUd56WmwAaEMbYCTBbbjzX0ui8wi5bMiIrWbtGpX1byWd35dU1Z6XwBeBTLLOH8XcK2I2ESkHdCfkufoqiuqmt/PgAwgETgBvKKUOlsD6awOVc1rQbdSRwJCGaqa386AEpHVIrJDRGZW5E0rNXVFXSMi3wGtSnjpaaVUaSOfS7rxufre3qmUShCRIOBz4G7g/aqntOo8lNeyzq9R1Zlfs664o1JqhohEl/G272IU4bdhzJn1C8acXTXOQ/kdCDiA1kBz4CcR+U4pdbTiKa88D+XV9d6DgEylVKl179XNQ/m1YVRtX4YRONaKyHal1Nqy0lqvA4JSauQlnBZP4ae+CIyR1iilEszvF0XkY4w/mDoREDyU11LPr2nVnN8hQH8RicP4Px8qIuuUUlcWeU87MMO1LSK/AIcvIR2V5on8YrQhrFJK5QHJIvIzRhVZjQYED+XV5TZquXTgofzGAz8qpVIBxJg9oh9mlWhZiW3QXxRvrOlB4caao4DV/HBbmMd4YRSnHyrjupOpI43KNZXX0s73dD4rm98i50RTekOcPxBg/jwKWO/pPNZwfv+MMeWMYHSW2A/08nQ+ayKv5usWjBtle0/nrxZ+t82BHeb/aRvwHTC+3HR4+oOowQ/4evOXnwMkAasLvPY0Rqv9QWCsuS8Ao1fJbmAfxrQcrh45E4H/V+D8OOAskG6+R/cGnNdi53v6q7L5LXJuoT+igvk1XzuIsarfdxhTBjfk/AYCS83/A/uBJxpqXs3tK4FNns5jLeb3LvN3uxd4uSLp0VNXaJqmaUDj7GWkaZqmlUAHBE3TNA3QAUHTNE0z6YCgaZqmATogaJqmaSYdEDRN0zRABwRN0zTN9P8BzFHmK4EjFJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 15 unionized from precincts, with 10 unmatched precincts center-numbered\n",
      "2nd plot above is same county 16 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 17 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 13 more precincts than census vtd tracts for county 17\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 17 unionized from precincts, with 13 unmatched precincts center-numbered\n",
      "2nd plot above is same county 18 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 19 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 20 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 12 more precincts than census vtd tracts for county 20\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 20 unionized from precincts, with 12 unmatched precincts center-numbered\n",
      "2nd plot above is same county 21 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 22 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 23 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 24 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 25 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 26 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 27 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 28 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 29 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 51 more precincts than census vtd tracts for county 29\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 29 unionized from precincts, with 51 unmatched precincts center-numbered\n",
      "2nd plot above is same county 30 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 31 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 32 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 33 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 34 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 9 more precincts than census vtd tracts for county 34\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 34 unionized from precincts, with 9 unmatched precincts center-numbered\n",
      "Here is county 35 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 1 more precincts than census vtd tracts for county 35\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 35 unionized from precincts, with 1 unmatched precincts center-numbered\n",
      "2nd plot above is same county 36 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 37 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 38 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 39 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 40 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 41 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 42 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "Here is county 43 unionized from tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expected 1 more precincts than census vtd tracts for county 43\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd plot above is same county 43 unionized from precincts, with 1 unmatched precincts center-numbered\n",
      "2nd plot above is same county 44 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 45 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 46 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 47 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 48 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 49 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 50 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 51 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 52 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 53 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 54 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 55 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 56 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 57 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 58 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 59 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 60 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 61 unionized from precincts, with 0 unmatched precincts center-numbered\n",
      "2nd plot above is same county 62 unionized from precincts, with 0 unmatched precincts center-numbered\n"
     ]
    }
   ],
   "source": [
    "#NOTE, I RERAN THIS AFTER FIXING COUNTIES 19, 58, 26 AND 39 WITH BELOW CODE BLOCKS\n",
    "print(\"We have precincts with voters but no associated vtd tracts.  We will build geoms county-by-county to understand why\")\n",
    "vestCountyGeom = [dummyPoly]*nCounties\n",
    "tractCountyGeom = [dummyPoly]*nCounties\n",
    "nUnmatchedCountyPrecincts = [0]*nCounties  #the number of precincts in this county that did not match tract names\n",
    "for c in range(nCounties):  #nCounties\n",
    "    for t in range(nTracts):\n",
    "        if countyNo[t] == c:\n",
    "            if tractCountyGeom[c] == dummyPoly:\n",
    "                tractCountyGeom[c] = tractGeom[t]\n",
    "            else:\n",
    "                tractCountyGeom[c] = tractCountyGeom[c].union(tractGeom[t])\n",
    "    #for p in range(nPrecincts):\n",
    "        if vestCountyNo[p] == c:\n",
    "            if vestCountyGeom[c] == dummyPoly:\n",
    "                vestCountyGeom[c] = vestGeom[p]\n",
    "            else:\n",
    "                vestCountyGeom[c] = vestCountyGeom[c].union(vestGeom[p])\n",
    "    if countyDifferential[c] > 0:\n",
    "        print(\"Here is county\",c,\"unionized from tracts\")\n",
    "        plotPoly(tractCountyGeom[c])\n",
    "        plt.show()\n",
    "        print(\"We expected\",countyDifferential[c],\"more precincts than census vtd tracts for county\",c)\n",
    "    \n",
    "    for p in range(nPrecincts):\n",
    "        if vestCountyNo[p] == c and isNoncensusPrecinct[p] == 1:\n",
    "            nUnmatchedCountyPrecincts[c] +=1\n",
    "            plotPoly(vestGeom[p])\n",
    "            plotCenter(p,vestGeom[p])\n",
    "    if nUnmatchedCountyPrecincts[c] > 0:\n",
    "        plotPoly(vestCountyGeom[c])\n",
    "        plt.show()\n",
    "    print(\"2nd plot above is same county\",c,\"unionized from precincts, with\",nUnmatchedCountyPrecincts[c],\n",
    "          \"unmatched precincts center-numbered\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d3a739c5-ee4c-4bb0-937c-a623faada438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now assign one-precinct counties to their vtds that were missed in initial matching\n",
      "55 Hinsdale 1 tract in county 26 has population 788\n",
      "2098 Hinsdale 001 precinct in county 26 with Trump Vote 353\n",
      "3086 Mineral 1 tract in county 39 has population 865\n",
      "2157 Mineral 001 precinct in county 39 with Trump Vote 427\n",
      "matching precinct 2098 in county 26 to tract 55\n",
      "matching precinct 2157 in county 39 to tract 3086\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now assign one-precinct counties to their vtds that were missed in initial matching\")\n",
    "emptyCountyList = [26,39]\n",
    "updatePrecinctList = []\n",
    "updateVTDlist = []\n",
    "for emptyCounty in emptyCountyList:\n",
    "    for t in range(nTracts):\n",
    "        if countyNo[t] == emptyCounty:\n",
    "            print(t,tractNAME20[t],\"tract in county\",emptyCounty,\"has population\",tractPop[t])\n",
    "            updateVTDlist.append(t)\n",
    "    for p in range(nPrecincts):\n",
    "        if vestCountyNo[p] == emptyCounty:\n",
    "            print(p,vestNAME[p],\"precinct in county\",emptyCounty,\"with Trump Vote\",vestTrump[p])\n",
    "            updatePrecinctList.append(p)\n",
    "\n",
    "for p in updatePrecinctList:\n",
    "    t = updateVTDlist[updatePrecinctList.index(p)]\n",
    "    precinctVTDno[p] = t\n",
    "    print(\"matching precinct\",p,\"in county\",vestCountyNo[p],\"to tract\",t)\n",
    "    isNoncensusPrecinct[p] = 0\n",
    "    nNoncensusPrecincts -=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f0afb1b4-86af-48e3-8734-19032de0fded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, to identify and fix the mismatches in counties [19, 58]\n",
      "vtd 143 Elbert 14 is in county 19\n",
      "vtd 144 Elbert 15 is in county 19\n",
      "vtd 145 Elbert 11 is in county 19\n",
      "vtd 146 Elbert 13 is in county 19\n",
      "vtd 147 Elbert 3 is in county 19\n",
      "vtd 148 Elbert 4 is in county 19\n",
      "vtd 149 Elbert 1 is in county 19\n",
      "vtd 150 Elbert 2 is in county 19\n",
      "vtd 151 Elbert 12 is in county 19\n",
      "vtd 152 Elbert 8 is in county 19\n",
      "vtd 153 Elbert 7 is in county 19\n",
      "vtd 154 Elbert 9 is in county 19\n",
      "vtd 155 Elbert 6 is in county 19\n",
      "vtd 156 Elbert 10 is in county 19\n",
      "vtd 157 Elbert 5 is in county 19\n",
      "precinct 2073 Elbert 001 is in county 19\n",
      "precinct 2074 Elbert 003 is in county 19\n",
      "precinct 2075 Elbert 004 is in county 19\n",
      "precinct 2076 Elbert 009 is in county 19\n",
      "precinct 2077 Elbert 006 is in county 19\n",
      "precinct 2078 Elbert 007 is in county 19\n",
      "precinct 2079 Elbert 008 is in county 19\n",
      "precinct 2080 Elbert 010 is in county 19\n",
      "precinct 2081 Elbert 011 is in county 19\n",
      "precinct 2082 Elbert 012 is in county 19\n",
      "precinct 2083 Elbert 013 is in county 19\n",
      "precinct 2084 Elbert 015 is in county 19\n",
      "precinct 2085 Elbert 014 is in county 19\n",
      "precinct 2380 Elbert 002 is in county 19\n",
      "precinct 2381 Elbert 005 is in county 19\n",
      "vtd 282 Summt 001 is in county 58\n",
      "vtd 283 Summt 018 is in county 58\n",
      "vtd 284 Summt 003 is in county 58\n",
      "vtd 285 Summt 004 is in county 58\n",
      "vtd 286 Summt 016 is in county 58\n",
      "vtd 287 Summt 005 is in county 58\n",
      "vtd 288 Summt 007 is in county 58\n",
      "vtd 289 Summt 015 is in county 58\n",
      "vtd 290 Summt 002 is in county 58\n",
      "vtd 291 Summt 017 is in county 58\n",
      "vtd 292 Summt 019 is in county 58\n",
      "vtd 293 Summt 008 is in county 58\n",
      "vtd 294 Summt 006 is in county 58\n",
      "vtd 295 Summt 012 is in county 58\n",
      "vtd 296 Summt 011 is in county 58\n",
      "vtd 297 Summt 009 is in county 58\n",
      "vtd 298 Summt 013 is in county 58\n",
      "vtd 299 Summt 020 is in county 58\n",
      "vtd 300 Summt 010 is in county 58\n",
      "vtd 301 Summt 014 is in county 58\n",
      "precinct 2303 Summit 004 is in county 58\n",
      "precinct 2304 Summit 005 is in county 58\n",
      "precinct 2305 Summit 006 is in county 58\n",
      "precinct 2306 Summit 007 is in county 58\n",
      "precinct 2307 Summit 009 is in county 58\n",
      "precinct 2308 Summit 010 is in county 58\n",
      "precinct 2309 Summit 011 is in county 58\n",
      "precinct 2310 Summit 012 is in county 58\n",
      "precinct 2311 Summit 013 is in county 58\n",
      "precinct 2312 Summit 016 is in county 58\n",
      "precinct 2313 Summit 017 is in county 58\n",
      "precinct 2314 Summit 019 is in county 58\n",
      "precinct 2382 Summit 020 is in county 58\n",
      "precinct 2383 Summit 014 is in county 58\n",
      "precinct 2384 Summit 015 is in county 58\n",
      "precinct 2385 Summit 008 is in county 58\n",
      "precinct 2386 Summit 001 is in county 58\n",
      "precinct 2387 Summit 002 is in county 58\n",
      "precinct 2388 Summit 003 is in county 58\n",
      "precinct 2389 Summit 018 is in county 58\n",
      "matching precinct 2073 in county 19 Elbert 001 to Elbert 1 tract 149\n",
      "matching precinct 2380 in county 19 Elbert 002 to Elbert 2 tract 150\n",
      "matching precinct 2074 in county 19 Elbert 003 to Elbert 3 tract 147\n",
      "matching precinct 2075 in county 19 Elbert 004 to Elbert 4 tract 148\n",
      "matching precinct 2381 in county 19 Elbert 005 to Elbert 5 tract 157\n",
      "matching precinct 2077 in county 19 Elbert 006 to Elbert 6 tract 155\n",
      "matching precinct 2078 in county 19 Elbert 007 to Elbert 7 tract 153\n",
      "matching precinct 2079 in county 19 Elbert 008 to Elbert 8 tract 152\n",
      "matching precinct 2076 in county 19 Elbert 009 to Elbert 9 tract 154\n",
      "matching precinct 2080 in county 19 Elbert 010 to Elbert 10 tract 156\n",
      "matching precinct 2081 in county 19 Elbert 011 to Elbert 11 tract 145\n",
      "matching precinct 2082 in county 19 Elbert 012 to Elbert 12 tract 151\n",
      "matching precinct 2083 in county 19 Elbert 013 to Elbert 13 tract 146\n",
      "matching precinct 2085 in county 19 Elbert 014 to Elbert 14 tract 143\n",
      "matching precinct 2084 in county 19 Elbert 015 to Elbert 15 tract 144\n",
      "matching precinct 2386 in county 58 Summit 001 to Summt 001 tract 282\n",
      "matching precinct 2387 in county 58 Summit 002 to Summt 002 tract 290\n",
      "matching precinct 2388 in county 58 Summit 003 to Summt 003 tract 284\n",
      "matching precinct 2303 in county 58 Summit 004 to Summt 004 tract 285\n",
      "matching precinct 2304 in county 58 Summit 005 to Summt 005 tract 287\n",
      "matching precinct 2305 in county 58 Summit 006 to Summt 006 tract 294\n",
      "matching precinct 2306 in county 58 Summit 007 to Summt 007 tract 288\n",
      "matching precinct 2385 in county 58 Summit 008 to Summt 008 tract 293\n",
      "matching precinct 2307 in county 58 Summit 009 to Summt 009 tract 297\n",
      "matching precinct 2308 in county 58 Summit 010 to Summt 010 tract 300\n",
      "matching precinct 2309 in county 58 Summit 011 to Summt 011 tract 296\n",
      "matching precinct 2310 in county 58 Summit 012 to Summt 012 tract 295\n",
      "matching precinct 2311 in county 58 Summit 013 to Summt 013 tract 298\n",
      "matching precinct 2383 in county 58 Summit 014 to Summt 014 tract 301\n",
      "matching precinct 2384 in county 58 Summit 015 to Summt 015 tract 289\n",
      "matching precinct 2312 in county 58 Summit 016 to Summt 016 tract 286\n",
      "matching precinct 2313 in county 58 Summit 017 to Summt 017 tract 291\n",
      "matching precinct 2389 in county 58 Summit 018 to Summt 018 tract 283\n",
      "matching precinct 2314 in county 58 Summit 019 to Summt 019 tract 292\n",
      "matching precinct 2382 in county 58 Summit 020 to Summt 020 tract 299\n"
     ]
    }
   ],
   "source": [
    "mismatchCountyList = [19, 58]\n",
    "print(\"Now, to identify and fix the mismatches in counties\",mismatchCountyList)\n",
    "for c in mismatchCountyList:\n",
    "    for t in range(nTracts):\n",
    "        if countyNo[t] == c:\n",
    "            print(\"vtd\",t,tractNAME20[t],\"is in county\",c)\n",
    "    for p in range(nPrecincts):\n",
    "        if vestCountyNo[p] == c:\n",
    "            print(\"precinct\",p, vestNAME[p],\"is in county\",c)\n",
    "sortedVTDlist = [149,150,147,148,157,155,153,152,154,156,145,151,146,143,144]\n",
    "sortedPrecinctList = [2073,2380,2074,2075,2381,2077,2078,2079,2076,2080,2081,2082,2083,2085,2084]\n",
    "for p in sortedPrecinctList:    \n",
    "    t = sortedVTDlist[sortedPrecinctList.index(p)]\n",
    "    precinctVTDno[p] = t\n",
    "    print(\"matching precinct\",p,\"in county\",vestCountyNo[p],vestNAME[p],\"to\",tractNAME20[t], \"tract\",t)\n",
    "    isNoncensusPrecinct[p] = 0\n",
    "    nNoncensusPrecincts -=1\n",
    "sortedVTDlist = [282,290,284,285,287,294,288,293,297,300,296,295,298,301,289,286,291,283,292,299]\n",
    "sortedPrecinctList = [2386,2387,2388,2303,2304,2305,2306,2385,2307,2308,2309,2310,2311,2383,2384,2312,2313,2389,2314,2382]    \n",
    "for p in sortedPrecinctList:    \n",
    "    t = sortedVTDlist[sortedPrecinctList.index(p)]\n",
    "    precinctVTDno[p] = t\n",
    "    print(\"matching precinct\",p,\"in county\",vestCountyNo[p],vestNAME[p],\"to\",tractNAME20[t], \"tract\",t)\n",
    "    isNoncensusPrecinct[p] = 0\n",
    "    nNoncensusPrecincts -=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f2b4adf-9104-43d6-8b11-145487ef4d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "below block will reassign unmatched precinct voters to nearby precincts, so we will check that county totals are unchanged\n"
     ]
    }
   ],
   "source": [
    "origCountyBidenVoters = [0.]*nCounties\n",
    "origCountyTrumpVoters = [0.]*nCounties\n",
    "print(\"below block will reassign unmatched precinct voters to nearby precincts, so we will check that county totals are unchanged\")\n",
    "for p in range(nPrecincts):\n",
    "    origCountyBidenVoters[vestCountyNo[p]] += vestBiden[p]\n",
    "    origCountyTrumpVoters[vestCountyNo[p]] += vestTrump[p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "49aeb41c-7de4-477d-82a6-1e4f82b4e08b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now I will assign these extra VEST precincts' voters to nearby census vtd tracts and zero out that precinct's totals\n",
      "working on county 0 containing 4 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 6 containing 6 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 15 containing 10 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 17 containing 13 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 20 containing 12 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 29 containing 51 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 34 containing 9 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 35 containing 1 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on county 43 containing 1 unmatched precincts\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets confirm all counties still have the same number of Biden and Trump voters after re-assignation\n",
      "uh oh!  county 0 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 6 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 15 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 17 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 20 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 29 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 34 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 35 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "uh oh!  county 43 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n"
     ]
    }
   ],
   "source": [
    "print(\"Now I will assign these extra VEST precincts' voters to nearby census vtd tracts and zero out that precinct's totals\")\n",
    "for c in range(nCounties):\n",
    "    if nUnmatchedCountyPrecincts[c] > 0:\n",
    "        print(\"working on county\",c,\"containing\",nUnmatchedCountyPrecincts[c],\"unmatched precincts\")\n",
    "        for p in range(nPrecincts):           \n",
    "            if vestCountyNo[p] == c and isNoncensusPrecinct[p] == 1:\n",
    "                neighborPrecinctList = []\n",
    "                #plotPoly(vestCountyGeom[c])\n",
    "                plotCenter(p,vestGeom[p])\n",
    "                plotPoly(vestGeom[p].buffer(-0.001))\n",
    "                for nP in range(nPrecincts):\n",
    "                    if vestCountyNo[nP] == c and isNoncensusPrecinct[nP] != 1:\n",
    "                        if vestGeom[nP].intersects(vestGeom[p]):\n",
    "                            plotPoly(vestGeom[nP])\n",
    "                            neighborPrecinctList.append(nP)\n",
    "                #plt.show()\n",
    "                if len(neighborPrecinctList) == 0:\n",
    "                    print(\"uh oh, no in-county neighboring precincts for precinct\",p, \"in county\",c)\n",
    "                else:\n",
    "                    for nP in neighborPrecinctList:\n",
    "                        vestBiden[nP] += vestBiden[p]/float(len(neighborPrecinctList))                    \n",
    "                        vestTrump[nP] += vestTrump[p]/float(len(neighborPrecinctList))\n",
    "                    vestBiden[p] = 0.\n",
    "                    vestTrump[p] = 0.\n",
    "        plotPoly(vestCountyGeom[c])\n",
    "        plt.show()\n",
    "print(\"lets confirm all counties still have the same number of Biden and Trump voters after re-assignation\")\n",
    "countyBidenVoters = [0.]*nCounties\n",
    "countyTrumpVoters = [0.]*nCounties\n",
    "for p in range(nPrecincts):\n",
    "    countyBidenVoters[vestCountyNo[p]] += vestBiden[p]\n",
    "    countyTrumpVoters[vestCountyNo[p]] += vestTrump[p]\n",
    "for c in range(nCounties):\n",
    "    if abs(countyBidenVoters[c] - origCountyBidenVoters[c]) + abs(countyTrumpVoters[c] - origCountyTrumpVoters[c]) > 0.001 :\n",
    "        print(\"uh oh!  county\",c,\"has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3f8962b5-3370-4204-a93d-5d35c3fb3dd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uh oh!  county 0 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "134197.0 134202.0 95655.0 95657.0\n",
      "uh oh!  county 6 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "188161.0 188166.0 58783.0 58796.0\n",
      "uh oh!  county 15 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "313276.0 313293.0 71601.0 71618.0\n",
      "uh oh!  county 17 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "104633.0 104653.0 121254.0 121270.0\n",
      "uh oh!  county 20 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "161918.0 161941.0 202810.0 202828.0\n",
      "uh oh!  county 29 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "218312.0 218396.0 148315.0 148417.0\n",
      "uh oh!  county 34 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "126105.0 126120.0 91480.0 91489.0\n",
      "uh oh!  county 35 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "3496.0 3497.0 4283.0 4284.0\n",
      "uh oh!  county 43 has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\n",
      "3876.0 3876.0 9592.0 9593.0\n"
     ]
    }
   ],
   "source": [
    "for c in range(nCounties):\n",
    "    if abs(countyBidenVoters[c] - origCountyBidenVoters[c]) + abs(countyTrumpVoters[c] - origCountyTrumpVoters[c]) > 0.001 :\n",
    "        print(\"uh oh!  county\",c,\"has a mismatch in Biden or Trump voters after re-assigning unmatched precinct voters\")\n",
    "        print(countyBidenVoters[c],origCountyBidenVoters[c],countyTrumpVoters[c],origCountyTrumpVoters[c])\n",
    "#NOT SURE HOW WE LOST A FEW VOTERS.  << 0.1%, SO CARRY ON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d99ef1af-b272-4875-8846-84a51bcede32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empty precinct 191 with inhabitants = 9\n",
      "empty precinct 490 with inhabitants = 4\n",
      "empty precinct 494 with inhabitants = 0\n",
      "empty precinct 496 with inhabitants = 0\n",
      "empty precinct 497 with inhabitants = 0\n",
      "empty precinct 499 with inhabitants = 0\n",
      "empty precinct 528 with inhabitants = 29\n",
      "empty precinct 534 with inhabitants = 0\n",
      "empty precinct 539 with inhabitants = 0\n",
      "empty precinct 549 with inhabitants = 0\n",
      "empty precinct 550 with inhabitants = 0\n",
      "empty precinct 596 with inhabitants = 26\n",
      "empty precinct 615 with inhabitants = 12\n",
      "empty precinct 623 with inhabitants = 3\n",
      "empty precinct 1873 with inhabitants = 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA+WUlEQVR4nO3deXwTdfrA8c+TpActlBsEKhYUBATkqAqLB6Kwive16nquB+v+VFAXb13vRXZ1PRZWl8UD8cBbUFFBWMCTlUMRQUGUu1Du0kKPJM/vj0nTpE1LShuaps/79SrJzHy/M09C5sk335n5jqgqxhhjEperrgMwxhgTW5bojTEmwVmiN8aYBGeJ3hhjEpwlemOMSXCeug4gklatWmlWVlZdh2GMMfXGwoULt6pq60jL4jLRZ2VlsWDBgroOwxhj6g0RWVPZMuu6McaYBGeJ3hhjEpwlemOMSXCW6I0xJsFZojfGmAQXdaIXEbeILBaRDwLTF4jIDyLiF5HsKuqdIiI/icjPInJHbQRtjDEmetVp0Y8ClodMLwXOBeZVVkFE3MB44FSgB3CxiPTYjziNMcbsp6jOoxeRTOA04BHgFgBVXR5YVlXVo4GfVfWXQNkpwFnAsv0PuQ6pOn9U8VharqoyoUNDqx/8XvD7nEf1Oc+DZf3h9dRfbl3+wGarWl5V/fLPo6lPNdZffnn59yKK+EoFP2sS/rzSZQfAARvm+wBtJ9FeT5jSz0akz0e5eCq8D/taHm2ZCOVCHXIsHDKw8uX7KdoLpp4EbgOaVHP9HYB1IdPrgWOquY74MONe+PLpuo7CGJPIOmTDtbNqfbX7TPQicjqQq6oLRWRwNdcfxVdncDsjgBEAHTt2rOZmDoAtPzmPg+8krGUglJsu37KM4tHlBpen7FHczrIK5V3h88QVYTn7WF5V/fLPo6lPNeOrZFtV1Qcq/FoKPq9i2QFr2R+g7djr2X8VfnlH2HaFeKR6y6MtE7Ec8NpFULA1cvkaiqZFPwg4U0SGA6lAhoi8rKqXRlF3PXBwyHQmsDFSQVWdAEwAyM7Ojr/bXonAQb1hsB1PNsbEgLiIVZfWPg/GquqdqpqpqlnARcDsKJM8wDdAFxHpJCLJgfrT9jvaOiXUTb+iMaZhiN0vnP0+j15EzhGR9cBA4EMR+SQwv72ITAdQVS9wA/AJzhk7b6jqDzUPuw6IWJ43xsSOSMwOhldr9EpVnQPMCTx/F3g3QpmNwPCQ6enA9JoEGR+sRW+MiaXY5Ri7MjZaMfy2NcaYWPYaWKKvFkv0xpj6xxJ9tKxFb4yJKeu6iQPWR2+MiaEYNiYt0UfLWvTGmJizRF/HrEVvjIkhv8+5Oj4GLNFHy1r0xph6yhJ91KxFb4yJIZfb+ujrnLXojTGxJFI2DHgts0QfNWvRG2NiyO93Rq6NAUv00VJfzP4TjDEGf4kdjK1zfp8zVrwxxsSC3xuzHGOJPmrWbWOMiaEYHgO0RB+tA3rHImNMg+NOcrpvYsASfbVYojfGxIgrCXze2Kw6JmtNRNaiN8bEktvj9NPHgCX6qFVyQ2FjjKkNLuu6qXuqlueNMbHjtq6bOGGZ3hgTIzFsTEad6EXELSKLReSDwHQLEZkpIisDj80rqXeziPwgIktF5DURSa2t4A8sO73SGBNLseserk6LfhSwPGT6DmCWqnYBZgWmw4hIB2AkkK2qPQE3cNH+h1uH7GCsMSaWYphjokr0IpIJnAZMDJl9FjAp8HwScHYl1T1AIxHxAGnAxv2KtM7ZwVhjTCzVfYv+SeA2IHRotbaqmgMQeGxTvpKqbgAeA9YCOcAuVZ0RaQMiMkJEFojIgi1btkT/Cg4Ua9EbY2KpLlv0InI6kKuqC6u78kC//VlAJ6A9kC4il0Yqq6oTVDVbVbNbt25d3U0dANaiN8bEUt226AcBZ4rIamAKMEREXgY2i0g7gMBjboS6JwO/quoWVS0B3gF+UyuRH2jWojfGxFJdtuhV9U5VzVTVLJwDqbNV9VJgGnBFoNgVwNQI1dcCA0QkTUQEOInwA7r1jCV6Y0ys1H0ffSSPAkNFZCUwNDCNiLQXkekAqjofeAtYBHwf2N6EGkVcZ+z0SmNMDMWwRV+twY9VdQ4wJ/B8G04LvXyZjcDwkOn7gPtqEmRcsK4bY0wsxfDmRnZlbNTsYKwxJob8PmdgsxiwRB8ta9EbY2LJV+IMbBYDluijZi16Y0wM+UvsVoJxwVr0xphY8fucESxjwBJ9tGJ4P0djjHG6buxgbBywFr0xJkb8Xuujr3N2MNYYEys538He7dZ1U/fsYKwxJgaKC2DCibD9F2gU8bYeNRabQ7yJSBVc9r1ojKllxXuci6UGjYITbo/JJixzRc0OxhpjYsBX7Dy26AzJ6THZhCX66rA+emNMbfOXOI8xOhALluijZ6dXGmNiwed1Ht3JMduEJfqo2cFYY0wMlHbdxGicG7BEHz07vdIYEwvWdRNPrEVvjImBYNeNJfq6Zy16Y0wslLboLdHHC0v0xpha5ivturE++jhgZ90YY2JAfc5jPCR6EXGLyGIR+SAw3UJEZorIysBjxGt3RaSZiLwlIj+KyHIRGVhbwR9Q1nVjjIkF9TuPErt2d3XWPApYHjJ9BzBLVbsAswLTkTwFfKyq3YAjy62jHrGDscaYGPDHSaIXkUzgNGBiyOyzgEmB55OAsyPUywCOB54DUNViVd25/+HWIWvRG2NiIY5a9E8CtwH+kHltVTUHIPDYJkK9zsAW4IVAt89EEYk4mIOIjBCRBSKyYMuWLVG/gAPHWvTGmBiIh0QvIqcDuaq6cD/W7wH6Ac+oal+ggEq6eFR1gqpmq2p269at92NTMWYtemNMLMRDogcGAWeKyGpgCjBERF4GNotIO4DAY26EuuuB9ao6PzD9Fk7ir6cs0RtjalnpWTd1mehV9U5VzVTVLOAiYLaqXgpMA64IFLsCmBqh7iZgnYgcHph1ErCsNgI/8Oz0SmNMDJS26GN0v1io2Xn0jwJDRWQlMDQwjYi0F5HpIeVuBF4RkSVAH+CvNdhm3VGs68YYU/sOQNdNtc7QV9U5wJzA8204LfTyZTYCw0OmvwWyaxBjnLCDscaYGChN9DHML3ZlbLTsYKwxJhZK73URB6dXGuujN8bEQjDRW4s+PliL3hhT60obkZbo657dStAYEwsHoEUfu+HSEo4djDVx4pe5MO0G54YVkfp1IyaMCPMqzIpUJtp11UE5Eef1izvkucs5TbH0eaS/SpcH1h9s1IU07srPi9Twq3a9wPO8nAivtXZZoo+WHYw18SLnO9i5FnqcDcmNyy2sKgFVUS6aMvFWTv2BPw157gt57ncGDPP7QEsqXx46HdzHA4/lpyPNi1iGqsuE5hJ3EnQZBhkdIrwXtcMSfdSsRW/iROm45Wc8BY2a1Wkopn6wPvpoWYvexIvSRO/31m0cpt6wRB81a9GbOFF6qbwlehMlS/TVYS16Ew9KbyJtid5EyRJ9tOz0ShMvrOvGVJMl+qhZ142JE8FE76vbOEy9YYk+Wn5fTIcRNSZqpZ9DX0ndxmHqDUv00VJ1Lswwpq6Vfg7VWvQmOpboo6W+mI4uZ0zUrI/eVJNlrmj5feCyt8vEgeDpldaiN9GxzBUt9VvXjYkPwa4bf9XljAmwRB8t67ox8cJa9KaaLHNFy866MfHCrow11RR1ohcRt4gsFpEPAtMtRGSmiKwMPDaPtm69ZGfdmLhjF/GZ6FSnRT8KWB4yfQcwS1W7ALMC09HWrX+s68bEi9KWvCupbuMw9UZUmUtEMoHTgIkhs88CJgWeTwLOrkbd+sfvta4bEx98gUTvtlHGTXSibaI+CdwGhB7mb6uqOQCBxzbVqFuBiIwQkQUismDLli1RhnWAqIK3CDwpdR2JMeAPXBFrLXoTpX0mehE5HchV1YXVXXl16qrqBFXNVtXs1q1bV3dTseX3AgpuS/QmDpR23bgt0ZvoRPPbbxBwpogMB1KBDBF5GdgsIu1UNUdE2gG50dZV1Utr6wUcEL5i59GTXLdxGANlXTcu67ox0dlni15V71TVTFXNAi4CZgcS9TTgikCxK4Cp1ahbv5QmerclehMHgl03luhNdGpyGsmjwFARWQkMDUwjIu1FZHptBBc3SkcJtJ/KJh7Y59FUU7WaBKo6B5gTeL4NOClCmY3A8Krq1jvWojfxxE6vNNWUWL/9po2EdfMr/qSNeHeoSi42iVTWEr2JF34/fHiL89xa9CZKiZXof5oOBVug2+lVl4t479cI80LLZWZD1nE1Cs+YGivtn8/IhLQWdRuLqTcSK9G3OBRad4OLXqnrSIyJjdKrs7OvrNMwTP2SWNf0u5NsRD+T2EoTvd+GKDbRS6xELy67vZpJbKWJ3saiN9WQWIne5bYWvUlsIoBYg8ZUS2IlenHbDmASnzVoTDUlVqK3HcA0BOKyrhtTLYmV6MVtO4BJXEX5MOWSwHUddtMRE73ESvQul7XoTeIq3Ak/Bm7S9u2rdRqKqV8SK9FbH71JZE0zYdR3zvMO2XUbi6lXEivRWx+9SXTNsyApHVoeWteRmHoksRK9HaQyxpgKEizRW9eNMcaUl1iJ3uWxS8ONMaacBEv0rrKxuo0xxgCJluitj940BBGH2TamcomV6FXLBn0yxhgDVCPRi4hbRBaLyAeB6RYiMlNEVgYem0eoc7CI/FdElovIDyIyqjaDr0D91toxxphyqtP8HQUsD5m+A5ilql2AWYHp8rzAn1W1OzAAuF5EeuxvsPumRLxTlDGJJuLtMY2JLKpELyKZwGnAxJDZZwGTAs8nAWeXr6eqOaq6KPB8N84XRYcaxFs1xVr0xhhTTrQt+ieB24DQI51tVTUHnIQOtKlqBSKSBfQF5leyfISILBCRBVu2bIkyrPKsRW8aAvuMm+rZZ6IXkdOBXFVduL8bEZHGwNvATaqaF6mMqk5Q1WxVzW7duvX+bUjVWvTGGFNONDcHHwScKSLDgVQgQ0ReBjaLSDtVzRGRdkBupMoikoST5F9R1XdqK/DIrEVvjDHl7bNFr6p3qmqmqmYBFwGzVfVSYBpwRaDYFcDU8nVFRIDngOWq+o9ai7ryYC3PmwbCDsaa6NXkpPNHgaEishIYGphGRNqLyPRAmUHAZcAQEfk28De8RhFXyc6jN8aY8qLpuglS1TnAnMDzbcBJEcpsBIYHnn/OgWxjq/+Abs6YOmHHoUw1JVbz1w7GGmNMBYmV6O1grDHGVJBYid5a9KahsCtjTTUkVqK3Fr1pCKxBY6opsRK97QCmQbAGjamexEr0tgOYhsAaNKaaEivR2w5gGgIbjttUU2IlertgyjQI9jk31ZNYnxa1rhvTANiFgaaaEi/R209ak+jslpmmmhLs02ItetMAWB+9qabESvS1NHrlE088wRFHHEHPnj25+OKLKSws5P7776dDhw706dOHPn36MH26M27bzJkz6d+/P7169aJ///7Mnj275gEYUyVr0JjqqdagZvGv5jvAhg0bePrpp1m2bBmNGjXid7/7HVOmTAHg5ptvZvTo0WHlW7Vqxfvvv0/79u1ZunQpv/3tb9mwYUONYjCmSuq3rhtTLYn1aamlPnqv18vevXvxer3s2bOH9u3bV1q2b9++weVHHHEEhYWFFBUV1TgGY6pkXTemGhIr0ddCi75Dhw6MHj2ajh070q5dO5o2bcqwYcMAGDduHL179+aqq65ix44dFeq+/fbb9O3bl5SUlBrFYEylSse4sRa9qYbE+7Tsb0unpBBev4wdTx7H1H/9hV/vzGLjA4dTkLeLl19+mT/96U+sWrWKb7/9lnbt2vHnP/85rPoPP/zA7bffzr///e9aeBHGVEL9gSfWojfRS6xEX4MR/Tas/oLbtn7ByO9/peVBzWntyyEp93vO/e3xfPnll7Rt2xa3243L5eLaa6/lf//7X7Du+vXrOeecc3jppZc49NBDa+OVGBOZtejNfkiwg7HV9POnULAV1n7Fl+vm8VHjdPZ0EDbN3M6eYU/R6KMbmPX512QPOpGcnBzatWsHwLvvvkvPnj0B2LlzJ6eddhpjxoxh0KBBdflqTENQ2qK3Br2phgRM9FHuAXu2w8vnBSdPEWFS2uFs6H4wXU9vQb+L78BTUEDfE2HEiBFcc801fPvtt4gIWVlZwS6acePG8fPPP/PQQw/x0EMPATBjxgzatGlT66/MmLKbglumN9GLOtGLiBtYAGxQ1dNFpAXwOpAFrAZ+p6oVjlCKyCnAU4AbmKiqj9ZC3JWoRtdN6E9fdwpNrp7BB+37ONMXAwtfhPdHwS3jICWFyZMnR1zNPffcwz333LO/ARtTPcEWvXXdmOhV59MyClgeMn0HMEtVuwCzAtNhAl8O44FTgR7AxSLSY//Drbk5P+Xy7r3D2f7kb8pm+oogb2N4Qb/XeXQl4I8eU38F++itRW+iF1UWE5FM4DTgEeCWwOyzgMGB55OAOcDt5aoeDfysqr8E1jMlUG9ZTYKuIlLI+Q7GZgVaPOI8igSfD/D6SXVvgiKg5/nO8uR08KTCs8dB0W5nVYV5zuPj3aLYqbRaPybqRtwHSIUY9zvk0Iq1nRDj5H38719h7t+rKFBFnFWetFDD11fpums5ngNVB2DgDfDbR6qoG/+iba4+CdwGNAmZ11ZVcwBUNUdEInVKdwDWhUyvB46JtAERGQGMAOjYsWOUYZUzaCS06BT4D1XnZ64GHgPTyX5l4bqdtBh8PZ16Bw6ebv8V/jPESfgdBzrzivMhd3nI6WzUsBVVVd0qllW5yWrGI5VO7O9Kyi2q4TojVt+PdXoLYcdqSGsF6a1DVhXNuiKUiabePsuELo+UVKpaXm7dItDuSEjJqEFMsfh/3Me6az2eA1Bn7ljIjVG79ADaZ6IXkdOBXFVdKCKDq7n+SO9qxK9OVZ0ATADIzs7ev2ZFp+Odv8p4i3DlfEf//oHVr50PbbrDzHvB74PLp0JLOz3SGBOw4mNwJ9d1FDUWTYt+EHCmiAwHUoEMEXkZ2Cwi7QKt+XZAboS664GDQ6YzgY0Ryh0Y8/7u/EVy2MmW5I0x4XwlCXGcbp8HY1X1TlXNVNUs4CJgtqpeCkwDrggUuwKYGqH6N0AXEekkIsmB+tNqJfL9seIT53HA/8EFL4Yv+2XOgY7GGBPvfCXgTqrrKGqsJudoPQoMFZGVwNDANCLSXkSmA6iqF7gB+ATnjJ03VPWHmoVcA82znMfsq+GIc+DgkMMFJ95VJyEZY+KYrxhc9T/RV+s3iarOwTm7BlXdBpwUocxGYHjI9HRgek2CrDWN25Q9qsLVM5xvbEiIb21jTG1TcLnrOogaq/+dT9WxM3AC0KOBwwbH3waHnQQdB9RdTMYYE2MN6/K6TseFT8/7G8x+uG5iMcaYA6Rhtej3bHMeT7ij7CKqzifUaUjGGBNrDSvRl14te+KddR2JMaY+iJMLoWsq8RK931d2NeuaL2HyOaC+8DK1dMtBY0xDUP9zRWIl+vwt8M9+UJQXPr/3RZAbOKtz0E2W5At3wXPDIC8HinY5p5le8T4VxwYSe69MA5cYTfrESvS7c5wk374vdDvNmZfWCvpfaQkLYNVseOOKsi/Cdkc6g8Ctmw8PVzV+vgDqXD186dsHIlJj4kcC5I7ESvSlXTbH31qW6BPcVVddxQcffECbNm1YunQpALfeeivvv/0ayeLj0PYteeH282mWnsIrb7zN39/dCOmtwJXEklWfs2j2VPq4V1JcXMwNT77LnG9/wSXCI1cP47zjjyA4ONy8vzt35HqodaDV7yo75pGc5nwBHNSrTt8LY2pdDW5PGk8SK9E3wLvvXHnlldxwww1cfvnlwXlDTxrCmEb/weNSbp+5nTFjxjB2aCqXtIVL7uwJNyzg+2U/ctZZZ9Fn8BkAPHLffbQ5cigr3n8Yv9/P9u3boVWrsg31PA++fzMwIqi/bGTQ/Fz4/g1nBFBL9CbhKImQTxIr0TfAmzIcf/zxrF69OmzesJNOhK8VTryHAb068dbb78B9r4S9L6+99hoXX3xxcPr555/nxx9/BMDlctEqNMmDM8rnSX8Jn+fzwrt/dJ7blcUmUSVAOkmsRN8AW/QRld4dy5PC8y9O4sILL6zw5ff6668zdaozDt3OnTsBuPfee5kzZw6HHnoo48aNo23bthXXrQrL34fCnc6VxkvfcuaXjiNkTCJJkK6bxLoyNpjnLdEDPPLSp3g8Hi655JKwxfPnzyctLY2ePXsC4PV6Wb9+PYMGDWLRokUMHDiQ0aNHR173lp/gjctg2o3OlcXihpt/cFr8xiSk+p9PrEVfT+wuLMHnVxat3cGPm3bTvmkjTujq3D1p555ivH6lyOsjxeMGv49J3xbzwaqlzJq/FCn3xTdlypSwbpuWLVuSlpbGOeecA8AFF1zAc889FzmQkgLn8azxzk1ektIhvWXtv2Bj4kJitOgTK9EnaB/9+P/+zN8/+anS5d5dm8ndks/J/5jLrFsGM/uTTxj7RTFzXxlJWlpaWFm/38+bb77JvHnzgvNEhDPOOIM5c+YwZMgQZs2aRY8eldzD3RfoFmpyEDTbz1s+GlOfJEA+SaxEn6At+le+XkM6ezm/70G0zUglq2Ua67bvZc/GZUx5/G5+WrsF/54ilj58OhPXncA/3vqKomJl6I3/gEYvMmDAAJ599lkA5s2bR2ZmJp07dw7bxtixY7nsssu46aabaN26NS+88ELkYPyBYZ0TYIxuY/YpQfroEyvRB1v0dRtGbbu67QquLrrDuXVLOTdfALu1KQK4RUlN+pb/uz4V3BlwyRsVhmAePHgwX3/9dYX1HHLIIWGt/ErZ+P2mwan/CSWxEn2Ctuh/31VgLSw89HpK3OFdMemZR9Dr+HMOXDClZ/RYi940CNaijz8J2kffKMl5Pf3PGw1pLcoWbF4G370GMxaFj08T8dEVYR4Vy3QZ5lzp+tPHzjbC6gC5gZ8V7sT66BhTqQTIJ/vcW0UkFZgHpATKv6Wq94nIkcCzQGNgNXCJquZFqH8zcA3OV+P3wB9UtbDWXkGY0kQfB2eNqkLBVsJaBOmt9+9DUzq0Q/m630yEBc+Bp5GzHdWyR/WHz4vWmi8gtalzFWxlXEnQOMI59sYkmgbUR18EDFHVfBFJAj4XkY+AfwKjVXWuiFwF3ArcG1pRRDoAI4EeqrpXRN4ALgJerM0XEaRx1HUz51GY+2j4vIE3wG8fqf66fg30nZee8VLKXwKND4LRlZ+RE6TlvggifSE8PwxWznDKN+8E18wqrRxeJ6kRpGZU/3UYU+80kCEQVFWB/MBkUuBPgcNxWvoAM4FPKJfoQ7bRSERKgDRgYw1jrlxpS97vrbrcvhRsc7pIqtP6VoWfPnKSYOtusGEhJDeBofc7yz/8szMvLycQa+i6Q55Hmr/+G+fRVxy+Tb8f8jdBzhLn4KjL49zI2JUEGR3AFfLLJpohh89+FjYuBhTa9rTz442BhtF1AyAibmAhcBgwXlXni8hS4ExgKnABcHD5eqq6QUQeA9YCe4EZqjqjkm2MAEYAdOxYw/Oz8zc7CdXvhV3rYM92ZzheTzL0PL8sIUq5u7sX7oLxRznPM4+G0x6DFTOci4S8xeArgn5XON0WyWmQ0qSs7hdPwaf3ha+veRYcdY3zfMGLsPYr+Ee3/X9dKY3Dpz3JzuO/j6tY9pg/wamPVpxflYN6On/GGEcD6rpBVX1AHxFpBrwrIj2Bq4CnReQvwDSguHw9EWkOnAV0AnYCb4rIpar6coRtTAAmAGRnZ+/fu1t6yt97f6q8TLQ3A1//P/j38c5zV1LZ+ePfTCwrc/k058IhdxJsDtzYZNjDkJzuPG8bkjTPGhdoLUOFPvOwD5NGnp/R3uk7DzXkXujyWyc2v9e5u5avBN67Dgq2RPc6jTFV0Pg45ldD1Tp1QlV3isgc4BRVfQwYBiAiXYFIA8CfDPyqqlsC5d4BfgNUSPS1ol0fOHciFO8m2O1RsAXSWoInBdzJZQnR7w3cYjDkZ9mO1U7S7jjQ6WZpmgldTy3rwlg2DQpynS6anz+Fl84M3747GX5zY+TY2vdx/mpqz3bng5fSxOleOvyUimXmjk2ID6cxdU79CbEvRXPWTWugJJDkG+Ek77Ei0kZVc0XEBdyDcwZOeWuBASKShtN1cxKwoPbCL8ftgd4X1M66ugytOK9HILH3uxJ+nQvF+U4L2lfitKqbHbLv9Ua6X21RvnPgtrig7J63pQdK1e98Iakfln/gdB+VOuNp50sptSlhvwT8voToVzSmzjWURA+0AyYF+uldwBuq+oGIjBKR6wNl3gFeABCR9sBEVR0e6Mt/C1gEeIHFBLpn6jW3Bw47af/qTvk9/DzLSc6pTZ2Dt5uWlC1v3NY5dhC8i1Pg/HaX2xlbpijP+XWycy28P7Ly7XgG7198+7Bu3Touv/xyNm3ahMvlYsSIEYwaNcq5q9X775OcnMyhhx7KCy+8QLNmzYL11q5dS48ePbj//vsrjIx55pln8ssvvwTvkGVM3NAG0nWjqkuAvhHmPwU8FWH+RmB4yPR9wH3lyzUIfr9zH9vSFrnfBz9Nd5YV5MIhv3EOAHfoD50HO7dATGq07/WqOgecf50HRbvL5oe24g+L8IukFng8Hh5//HH69evH7t276d+/P0OHDmXo0KGMGTMGj8fD7bff7tzVauzYYL2bb76ZU089tcL63nnnHRo3blxhvjFxQf00iNMrzX7y+51x23/8oPIymdmV9+lXRcQ5CNz7d/sf335q164d7dq1A6BJkyZ0796dDRs2MGzYsGCZAQMG8NZbbwWn33vvPTp37kx6enrYuvLz8/nHP/7BhAkT+N3vDvxrMWaf1J8Q3aCW6GOk4N3RzFy8CeVoyHASY/ADowrFe+HrX5Bvbq9yPeKu7EPmzBeXO3SS8q0Pl0sizt+VswZ3UjLN23fC7UnC5XLhSUnG5fKQktwISUohJS0NjzuF5MZNSGmUis/vQj1pSEpj2maksCknhwXffMODD9zPyJE34nK5GXHttXw6axZ+v59u3brh8XjYuHEjS5YsYeJE54ylMWPG8Nxzz7Ft2zZuvPHG4FDKr7/+Oo888gg+n4/TTjuNv/3tb1W+NwkpeGEbBI+7hF7dHFhWsDsPb8leigoL8XmLAKWkyEuJdy/+4hK8xcV4/cX4Skrwep0T4tTnw+v14vOW4Pf58PuK8fv9+P0+QPGX+FGfD7968ft8qPrx+/2o3x9F2L7QqYrLo1gHPt++y4RyO10qEtK1IoFTpsXlBpHA51/Cyrnc7uA8V2l5j/Po8pSeci243C48BYdwfAJ03YjG4Xmi2dnZumBB7I7ZHggf3dmTU1PW1XUYMZNXBCdOymdEdjJ92rnp085NXjF0e3I3vQ9yMeo3yZzQOYm/fFrEqu0+urRyk+qBghKYs6qEx89M4+FPC1m308+Uy9O57JUC9pTAx3/MoEW6i5veyee8PikM6pwUSBsScSCHsq8vDZsu//VY+XyNOD/6dSkCiFZcVvZcK6xLyv0FR+8oXV+EcpBot4SrH94v7MwZjy7ed8E6JiILVTU70jJr0cfAunXreGDKCm7J97EHD0N7t+H0/u14cc46FqzaicclHNQshRuHdyI91UPeXi9/f+9nft5UwIk9WzFiqHP2TmGRlztf+zG43m27SzihRwuuOan0grL9/5LukraOPeph4962iISm0kBSEuf5RunMDmlDU91OOrvYIh1Rv4+XXp/KMT1a0qlnC/JE+awEZny/i2LN56Ts1mRkNWaxX5m7fgPrtvn57y8+3C7B61P6d0plxvpklm/Zy+5CP6f+p4DdhX4aJQv5KU3I90L3LOXNH4rJOiQtLGGG0nIpuPy7UdaGqayclJsOFb6sYhkJ20b5WMreTUDLbye0rlQRj1S9/ojLwr+6gs91H8sDVMu/25V93VW0z0+j7nsd1Sbl373KIon0hR5eTqRivTNSfyHNXVCDAOOEqsbdX//+/bU++/GH7/XDP2ao3peheXc00UNauPXrLz7TTz75REtKSlRV9bbbbtPbbrtNVVXz8/P1s88+02eeeUavv/76Stfbr18/nTt37j63v3btWh08eLB269ZNe/TooU8++aSqqo4ePVoPP/xw7dWrl5599tm6Y8cOVVXdunWrDh48WNPT0ytsf8A5V2ty09aanp6uqqpnjftMOw08VUeNGhVW7qOPPtLDDjtM27dvr7t27Qpbdvrpp+vkyZP1vvvu09/85jc6efLk4LKrrrpKx48fr926ddMOHTror7/+qiUlJXruuefq6aefvs/Xakws7b2/qc68q0tdhxEVYIFWklPtl2A1fP7pNN54YRyzP3yrynKH9+hJ+8ve5O09PWmSIhzZWpj/r3MYNHAgHo/zI2rAgAGsX78egPT0dI499lhSU1MrXefKlSvJzc3luOMiDHdQTumZMcuXL+frr79m/PjxLFu2jKFDh7J06VKWLFlC165dGTNmDACpqak89NBDPPbYYxXWtTq9O60vfRwFcvMKyV35Hb9+9REzZ82i15FH0uvII/nwww+5/vrrWbNmDcnJyRx//PFcd911ADzyyCNhNyjXCF2FIoLb7eaZZ57hwgsv5LjjjiMrKyv4XhlTV0QbyKBmxvHB6y/Sa3EnsjgSfoIlX37AIaMH0rR55IG/+gwaRp9Bw/jXX65jcc4EJh9cxP/+1ocTH1oFwPPPP8+FF14Y9fZfe+01Lrzwwgo3+o6kumfGlH7R/PzzzxXWldLBGZtnb7GPo/86C1KzyHxsJtv6tQqW+fngVhx22GFcf/313HLLLcH5kyZN4oMPPmDWrFmICPfffz8pKSmsW1d27GL9+vVcc801wXPozzjjDAAmTJiA211uLCJjDjAhMW49Yok+Sps2b6QPhwIw37OS7z1rafvSYv40yhmwc8ZrT7Nj8VQ8mdnBI/kFhXt5bMLL3HtqOhkpQl/ZDoS3ckMvQMrLywvey3X79u1ceOGFrF69mqysLNauXcurr77KzJkzueOOOyguLiY5OZm///3vDBkyBICFCxdy5ZVXsnfvXoYPH85TTz3FmjVrWLx4Mcccc0zY6xk/fjwbN26ke/fuwQufmjZtyhdffEG3bt2CFz598uQzLNvu5eKnXRyTN5ep77yGJLn545ix9Bw8hLtXrOfxi85jz+pV5OTkMGPGDF5++WUWLFjA2LFjmTt3btgNys8880x+//vfc8stt7Bx40ZWrlzJ0UcfDUBubi5t2rRhx44d/Otf/+KNN96I4f+oMftW/9vyAZX16dTlXzz20f/nyUd03e3zdN3t83TaXZP0vvvu05wNG4LLP7/3ENX7MoJ/xfc00WGHuvXxYSnBeWseaKkvvviiDhgwQAsKClRVdePGjbpw4UJVVf3Xv/6lTZs21R9++EFvvfVWHTNmjKqqjhw5Ups3b66qqosWLdINge1+//332r59+2AMRx11lH755Zfq9/v1lFNO0bffflv79eunb7/9dthrefjhh/WUU07RBQsWqKpqXl6edunSRR9++GE9+eST9fjjj9du3bppy5YtdciQIaqqmpSUpCkpKQpo04f+oZ06d9Ytewu10SVXK6ApKSmampqqgJ5yyinaqVMnTUpK0tTU1OBfab/+ww8/rJ07d9auXbvq9OnTg3FddNFF2r17d+3evbu+9tprtfVfZ8x+K74vQ2fcdXhdhxEVquijtxZ9lC666kbmP/kuhxd04iBXOm3ShIPatwfgs4+m0724hOXvtSetbREdB2/lgveLSW7Wit7ZGXxQmIZbvMzbcihTp4S3ckO7WRo1akTz5s3ZsGEDU6dOZc6cOQD4fD5cgbHl+/Ytu0j5iCOOoLCwkPy129m2ZRt5O3dxVLe++LYXctHZFzBq1Chuvvlmzj333GCd0O6U0hhKu3d27txJZmYmN954I/369ePVV1/luuuuY9myZagq//d//8e42fNwt2rDhhZt6TpxCk2u+CMy/V2WLV7Erl27OPbYYznnnHMYMWJE2PvXv3//YBx33303d999d4X3+LXXXquN/ypjao2QGCMVN6hE/8o9F9LZ9RUel5/Vew7jgr/Nibpu4yZNOOneywFY9dH7uOfl859bb6VVtx5s/OYQljCZE7mBPZtT+GKdj6nfFdGrVxtGvetlT14Ojz85njfvuIOioiKGDnWGJxgwYADPPuuMBZeVlcWOHTvYvXs3V1xxBfn5+cEvgOnTp1NSUlIhprfffpu+ffuy818/sDTnR1oXN2HT2G9QVV5449+4xR3WZ/7xxx8Hu1O2bdvGaaedxqZNm/B6vWzdupVTTjmFTz/9lN///vckJyeTm5tLp06d2LBhAwD9+vWjzxdfsvjma2jV5XCO1UIGZ7Xh2UM60rlzZ1SVxo0bc/XVV4fFWZ0DycbEEwn5tz5rUIm+rS5moGsXAFmNl/DGYzfh94UnUEE48fLbadOuwn1UAHjw3UsZ1PQrDj4mieWvHUbxhvVokz/Q1LOFrmfnsHann7s/KaJDi0bk5+1kVLdcRg1IYcQTl7Fnh5tWHTrTqVOn4KBf27Zt4/zzz2fLli2kp6fzwgsvcO6554YNCNazZ08+/PBDBg4cyJIlS/B6vTRp4tz05JNPPuE3w89i45ZNKMoVb93OlX3PYe7q/+FyuXC5XMEzXToEfoH079ePzbm5+P1+0tPS2FtYSElJSbDvv2XLlpx33nlMmzaNZcuWceWVV+L1ehk5ciSFhYWcMGgQeXl5XNS+FSe3acZzIqxYsYKTTz6ZgoIC/vCHP/DSSy8F46/OgWRj4okdjK1nPn39GRq5iynBuRdia7+P3+W/ELnwvyfzmb8FGwq70O2s0fQZVHa2SrcM5/6sKRkliNtPoW83LXQqbZrOxpPsp1G6nyeGpdKvnZtNxbs4/t8lDD3Uw/mdXdx4Uho/tTiVd79YzgP3388tf/4zl1xyCatXr8br9dK4cWPOPfdcbr31Vvbs2UP37t3JyMigVatWuN1O63zs2LEsW7aMHTt20Lp1a8aPH8/g3w5h3bp1fPbZZ3y5YTEFrfy8+uqrjHvwL/Rt15qfNm3hs5W/cvOxfSkoKuaJmZ8xMKsDLpeLhavX0y+zLTv2FHJC1078e+58Jk2axPjx4wEYOHAg8+bNY8yYMSxbtow5c+bQuXNn5s6dS/v27VmxYgXNmzdn69atNG7cmP79+7N4cfhVhFOmTGHy5Mmx+G81JqYS5fzzhE70Bbt3s+LxrrRwlXCyv6TC/9p/va3ZXtw2bN5BqesZ5NrJca7tkDYfZl7Am1P7wEFHgfrYU3wUa3rkkrS5EerbAcD2XWto3O8S3v5lOuL2c2yHFeD3sSG5MV1b7+H1LQfzx565tPcX0Wvns7hdJbw1u4SnUt9h9IPjeOv5t1i/fj2LFi0Knu/u9/vJyMjgP//5Dz6fj4yMDIYPH07jxo259tprKS4upnPnzsyYMYO5c+eydu1abr75ZtavX8+qVat4/PHHueT8C/CvWMJnqzdwaKfODPvjSJb++BOHr93C8nXr6NerFx33FlPk9bE5bzdnX34x06a9wqOPjuGgg9pRUFAQ7Gs/+eSTefjhh+nTpw95eXls3bqVo48+ms2bN7Ns2TKef/55zj77bMaNG8d5550XfD+/++47vF4v/fv3j+n/tTGmcgmd6D946BwuTNsDftjicrOspBkbi7uSorsoIY0Tb34xYhfNK/deRHJRDqenf0cjVS5I+xbyvi0rsBhe3XschR0OYceGNQAMveA6mrW8J2w9a0a2Z0mOj5vPzuPnkgzau53TK5//toQLj/CwpGkKLp+LyZMnk5mZyZ49ezjjjDP45z//yV133UXfvn0pKChgzZo1tGrVivT0dBYuXEhubi5er5edO3fi8/lo0qQJJ554IhMnTuSoo46iUaNGNGnShJH3PcgTl53Hqg2bOPqobHoNGUZm36P488Nj8Cl8/N85iAiqSvMmySzOHcv4v2xl+3Y/a9euw+Px8Nlnn3HppZfy+uuvc8YZZ/Dee+8xf/58GjduTO/evRkxYgT33nsvN9xwAwDJycnk5OSwc+dOmjVrxvPPP09xcTGNGzfmyiuvZNy4ccH3J9LpoNa9Y0ztS+hEnyo7AdjkctPk5jWc0KRJ1RUCLnloSvD5a7efQoruDE53TFtHNvn8vtFn+Bp9xhfpLcjrdC3NWrYKW8eHk5/mwbc28+QpqZyUmg9AjsvNnbPcbHI15ZK313CplI55ojz22GM88MADLF68mIyMDL799luOPPJIAIYMGcJzzz0HwE033cSECRPYtWsXL7zwAldddRVt2rQBnP56j8dD27Ztee6553B7PKyQRrjdLlzeEtauXIHLJdx562hG3vJnOmVlkb9rK4V7ihk4qAsXXng1zZpO4vrrv6NRIz+HZLZn/pefM/3Nl5j+/lRWrPoFn88ZhbCgoIA/XHkl48eP56677iIjI4NLL72UcePGhY1HP3XqVB555BF2795d4cYif/rTn5gwYQIDBgxg+PDhfPzxxxHHrDemLhTs3o0zsHb9b3wkXKJ/8x+j8e7dRUaHLqS69wBwkN/H1Fee4Kzr/lLt9V089uOw6ZVL/sebL9+KixJaJOdyrGcLOesfB+4KlikpKeHhB27l8l5JHNYjg3cKOlHcvA95zY/kp+JJzF04K6zlmp+fz7hx4zj22GPJyMgA4KuvvmLu3Ll4vV4WL15Mbm4uJ5xwAsnJyZSUlNCrVy/OPfdc7rzzzuCFSu+88w5JSUmMGjUqeOHVmtytHNKiGXu35fLmPc4ZOKqKx+WiuZbgTkqhc2YLfli+gtWrx5G+cjvdW7mYe6WH1ukbnQB/uJHhFwM4Nwi5f04hjZOF0cc15rPPuzNjxgxOOukknn76aSD8qtvVq1cD8OKLL4a9jzk5OeTl5TFw4EAALr/8ct577z1L9CZueEsKATu9Mu68cvcFXJI0w5lYDSSXLTth8xNA9RN9eV16H02Xv80NTs+6uyvHJW3m/tFDmH54LjdtHsAzb8/l8NYuhv6mKb3u20RvnFMbH7zlFub+97+k5S6C5HRo35eSkhLOO+88BgwYQKtWZb8K0tLSOOKII9hxcRPyl19Ei4/+SseOHVmwYAG9evWia9eugHOl6V133cX8+fNJSkqie/fu3HTTTQD4/X6+W7qU3l27kLdrF3vbZQGQv2cPWQdnsmjNWjxuN5sKChFXEhs2XMCDrz5NcbEydGpTEDcDeh3Ks3ddGT5G+sZ3YfUnrF62iAXf/I+NOZvo1q0b/fr1c0qocuutt1b5Pm7YsIHMzMzgdGZmZvA0TmPiQVFRYV2HUGsSKtF7SnZAEiygMV2lgHxx40Fp6fexqKQVQ2KwTT8ukoHpR2xmr8tF61+m8Pm3Bexq4+K8Z7aR9m4f/vrXvzJy5MjAOfQnQ+4yBmS6eWbiS1z9j+l89dVXeDweiouLee+997j33nt56qmnmP3f2ZzwmvNLYdvOfF5++WUAVq1axezZs5k+fTp//etfueqqqygsLMTlctGtWzeuu+46nn32WebNm8eOHTv4ctFiSkpK+HXTZh544AFeHz+ebXm78Xg8pKWl0bJlS2bPnk1WVhaXN9kN374Msgdu/RnSWlR4zfdPuJL8Bztywj3P88/jUjj3gQ+hm3P3yEceeYQFCxYEBzGrjEZoJln/vIknvuLADVsaQteNiKQC84CUQPm3VPU+ETkSeBbn9/xq4BJVzYtQvxkwEeiJc0rqVar6VW29gLBt4QXgJ/8JZD/4Khkhy2KR5AG05//x1uI3uPEHH3luL7ta9eHN0sZs+0Gcf5Nz39ThwwO30fWVwKQzYN3/+Hy9MnnyZHr16hW88jX0S+GU356CeAvp3HUzs378kcMPakZWVhZ5eXkUFxdTXFxM7969WbFiBQcffDBdunTB4/Hw9ddfM3HiRK655hqKiooqxHzttdfy3XffsXLlStasWUO7du045BBnDHxOvBM2fw8538HU6+HiilerlpSUcN7Mtlxydm/ObfOZc/9bKg5iVpXMzMzg6J3gDG7WPnCevzHxoKSkYbXoi4AhqpovIknA5yLyEfBPYLSqzhWRq4BbgXsj1H8K+FhVzxeRZCAtQplaEbxVgid5HyVrz7CLR8LFI6Ov4E6Cq5x+/2MB1YsrFAl+KURQ2uddXqQWcqUhuN3069cv2NUSpmkm9LnUSfSlNzIvt52rr76a7v0Hcct9N8FTR4IrKeyq29BBzCrTrl07mjRpwtdff80xxxzDSy+9xI037sf9c42JkZJAi75BHIwNDJaTH5hMCvwpcDhOSx9gJvAJ5RK9iGQAxwNXBtZVDBQTI4Jzz8ngfVRN9ZUUwkeBnyTDHqmw+Isvvgj+Cunz6SewNZ+/tl7KyCferHJ4h9JfIe+99x4zZsygR48ePPPMM8HTK0899VQ7EGviUgIci42uj16cO+4uBA4DxqvqfBFZCpwJTAUuACKNGdAZ2AK8EOjqWQiMUtUK9+YSkRHACICOHTuWX2wOlGmBVvWhQ+CYP1ZYfOyxx5b9eti8DJ4ZCIOPZvj1YypdZWW/QrKzsyuccmlMvPB7K44vVV9FdYWvqvpUtQ+QCRwtIj2Bq4DrRWQh0ITILXUP0A94RlX7AgXAHZVsY4KqZqtqduvWrav/SgBXoEXvSknfr/oG2LsdXElw6TtON1NV/IEdwX3gusqMOVC83tLjWw2g6yaUqu4UkTnAKar6GDAMQES6AqdFqLIeWK+q8wPTb1FJoq8NLnESfXJaxj5Kmkr9/Knz+OOHzqO4oOOAiGff4LNEbxJXwW7n3JKGctZNa6AkkOQbAScDY0WkjarmiogLuAfnDJwwqrpJRNaJyOGq+hNwErCsll9DUGmLXtd8yhu3fU7oHezD/7NC7xcv4eUini1SrkxIfREp68MTIfxHUuDKV3FGxXS25wqUo+yxtGxwvqvcI0i5H1/lP3ziKn9/+/DjFK7yv92kwgzE5eZ4t9tZ+1uXOVtU2JWUyS9pJ+ByexCXC3dyEuJy06x4A4eKsPLrT9m7Yituj4uk1MZ4PEmkNGqEx5NCSlo6SUmppDXJwJOUSnqUVyfXpoLdu4PPveXOpAg9V9pXXPajNPSMi5KQ+aE/58tafOAt8ZWtxxv+49YbUkdDlnlDnvu8fvy+EvyBX0l+rw+/z4ffX4IGrkb2+334/T7U78Xv8wfnq/pQvw/8fvwa2Af8OPPUF7j5hD/wAvwoCn6/U4jSA/kanPb7y453qV8RFCVQXwmWC27IWQlhvdml3XuqSKCuokiwSGjPtz9YPtLeF9ZHXmH/rKxTImQfl9C5oft+SN3Q9Qaeu7x7yU4tt4J6KpoWfTtgUqCf3gW8oaofiMgoEbk+UOYd4AUAEWkPTFTV0lNHbgReCZxx8wvwh1p9BSFKNAWAc9OWx2oT8ctfw+VVaFu0lq5FlY8+2X/Vs7Aq+vX5A3/lUkOV7abIX9Phzyvb5a0jrxaFtnkSXSA7enUfXZj1gFTntLwDJTs7WxcsWFDtegW7d/PeEzeBd2/ZzBi0OELfs7L2fOj6w99T0YplJELZSOuKNB62VJhTXrntVygf4WKlkO1KyPZT3IUkuYuDhZzWXdk6VYUib0pg/w/UEw2ur+xRAw2lstclUj7Vl0USjFQjL4vuUxtSvooKlf80r2x7kctXWSaq1xH+rgXLaPhXW3jbN6SO8x8TVl7LlSubV75+pBjC60jwefj2q/61XDqv9Ndt+fcuQpnQX8xVfnYrex6ZVPgQRK4fuk2/eOhz4d107xf/N80RkYWqmh1pWUJdGZvepAmX/OW5ug7DGGPiSqKMq2+MMaYSluiNMSbBWaI3xpgEZ4neGGMSnCV6Y4xJcJbojTEmwVmiN8aYBGeJ3hhjElxcXhkrIluANQdgU62ArQdgOzVhMdae+hCnxVg7GmKMh6hqxKF/4zLRHygisqCyS4bjhcVYe+pDnBZj7bAYw1nXjTHGJDhL9MYYk+AaeqKfUNcBRMFirD31IU6LsXZYjCEadB+9McY0BA29RW+MMQnPEr0xxiS4hEz0InKBiPwgIn4RyS637E4R+VlEfhKR34bMnxOY923gr00l645Y/0DEGLJ8mogsrWS9SSIySUS+F5HlInJnvMUYWN5bRL4KrP97EUmNtxgDZTqKSL6IjN6f+GIZo4gMFZGFgfdvoYgMibcYo6kfyxhF5GMR+S5Q79nALVHLr7dO95loYgyU2/99xrlxcGL9Ad2Bw4E5QHbI/B7Ad0AK0AnnTqfuwLKwspWst9L6ByLGwPJzgVeBpZWs9/fAlMDzNGA1kBVnMXqAJcCRgemW8fY+hpR7G3gTGH0gP49Rvo99gfaB5z2BDXEYY53uM0BG4FEC/5cXxds+E2WMNdpnErJFr6rLVfWnCIvOwvkPLVLVX4GfgaOrseqa1q9RjCLSGLgFeLiqVQPpIuIBGgHFQF6cxTgMWKKq3wW2s01VfXEWIyJyNs4N7X/Yn9hiHaOqLlbVjYHJH4BUEUmJpxirqn8gYlTV0s++B0gm8s1l63SfiTLGGu0zCZnoq9ABWBcyvT4wr9QLgW6be0Uq3MU4mvqxjvEh4HFgTxX13wIKgBxgLfCYqm6Psxi7Aioin4jIIhG5rZbjq3GMIpIO3A48EIPYStX0fQx1HrBYVYtqLzyg5jHW9T6DiHwC5AK7cfaP8up6n4kmxhrtM/X25uAi8ilwUIRFd6vq1MqqRZhX+u15iapuEJEmOD+fLgNeqkb9mMYoIn2Aw1T1ZhHJqmybOK0EH9AeaA58JiKfquovcRSjBzgWOAonScwS5w72s+IoxgeAJ1Q1P/J3flzEWLrtI4CxOK2+eIuxzvaZ4BPV3wb6s18BhgAzy5Wts32mGjFWa58pr94melU9eT+qrQcODpnOBDYG1rch8LhbRF7F+c8vn+grrX8AYhwI9BeR1Tj/b21EZI6qDi5X//fAx6paAuSKyBdANk4XRLzEuB6Yq6pbAURkOtAPiPihraMYjwHOF5G/Ac0Av4gUquq4OIoREckE3gUuV9VVVW2sDv+v62qfCV1voYhMw+lCKZ9E63KfiTbGau0zkYJO2D8qHhA5gvADIr8AbpwPaqtAmSScn07XRVhfxPoHIsZydbKo/ODX7cALOC2IdGAZ0DvOYmwOLMI58OUBPgVOi6cYy5W7nxocjI3h+9gsUP+82thfYhRjne0zQGOgXaCMB3gduCHC+upsn6lGjDXaZ2rlwxFvf8A5ON+ARcBm4JOQZXfjHPH+CTg1MC8dWIhzVPsH4CnKjoifCTxYVf0DEWO5umE7VmiMgQ/Om4HXsQy4Nd5iDExfGohxKfC3eIwxZP791Oysm1j9X9+D07f8bchfm3iKsS73GaAt8A1l+/U/AU887TPRxljTfcaGQDDGmATX0M66McaYBscSvTHGJDhL9MYYk+As0RtjTIKzRG+MMQnOEr0xxiQ4S/TGGJPg/h/zWWa+yblKKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p in range(nPrecincts):\n",
    "    if isNoncensusPrecinct[p] == 1 and vestBiden[p]+vestTrump[p] > 0:\n",
    "        print(\"failed to zero out precinct\",p,\"residual Biden and Trump voters\",vestBiden[p],vestTrump[p])\n",
    "    if vestBiden[p]+vestTrump[p] == 0 and isNoncensusPrecinct[p] == 0:\n",
    "        print(\"empty precinct\",p,\"with inhabitants =\",tractPop[precinctVTDno[p]])\n",
    "        plotPoly(tractGeom[precinctVTDno[p]])\n",
    "        plotCenter(precinctVTDno[p],tractGeom[precinctVTDno[p]])\n",
    "        plotPoly(vestCountyGeom[vestCountyNo[p]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a03fcfd1-9358-4f42-8509-5fa82bca81a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now finalize the Biden and Trump voter pops for all 3108 census vtds\n",
      "We have a total of 1804182.0 Biden voters vs. 1804352 in state CO via uselectionatlas.org\n",
      "We have a total of 1364428.0 Trump voters vs. 1364607 in state CO via uselectionatlas.org\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now finalize the Biden and Trump voter pops for all\",nTracts,\"census vtds\")\n",
    "vtdTrump = [0.]*nTracts\n",
    "vtdBiden = [0.]*nTracts\n",
    "for p in range(nPrecincts):\n",
    "    vtdTrump[precinctVTDno[p]] += vestTrump[p]\n",
    "    vtdBiden[precinctVTDno[p]] += vestBiden[p]\n",
    "print(\"We have a total of\",np.sum(vtdBiden),\"Biden voters vs.\", 1804352,\"in state\",STATE,\"via uselectionatlas.org\"  )\n",
    "print(\"We have a total of\",np.sum(vtdTrump),\"Trump voters vs.\", 1364607,\"in state\",STATE,\"via uselectionatlas.org\"  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "085bb0ec-fe29-481f-9490-90c98ec816ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "skipList = []  #Colorado has no lakeshore tracts.  See Ohio code for how to treat these\n",
    "isSkippedTract = [0]*nTracts  #DO NOT RUN THIS BLOCK IF WE ID'D SKIPPED LAKESHORE TRACTS IN ABOVE BLOCKS *****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bd5bcf66-110a-4d5e-8af1-2c372476f8c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have pushed island precincts into the map and eliminated lakeshore tracts. Now build the county-level map\n",
      "working on tract 0\n",
      "working on tract 500\n",
      "working on tract 1000\n",
      "working on tract 1500\n",
      "working on tract 2000\n",
      "working on tract 2500\n",
      "working on tract 3000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total of all county pops is 5773714.0 vs a state pop of 5773714\n"
     ]
    }
   ],
   "source": [
    "print(\"We have pushed island precincts into the map and eliminated lakeshore tracts. Now build the county-level map\")\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1)])\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "countyPop = [0.]*nCounties\n",
    "nCountyTracts = [0]*nCounties  #how many tracts found in this county\n",
    "countyTractList = [\"empty\"]*nCounties  #list of this county's tracts\n",
    "for t in range(nTracts):  #now loop through tracts, assign each to county, build county polygons\n",
    "    if t%500 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    c = int(countyNo[t])\n",
    "    nCountyTracts[c] +=1   #found another tract that belongs to this county (we include skipped tracts here)\n",
    "    countyPop[c] += tractPop[t]\n",
    "    if t not in skipList:\n",
    "        if countyGeom[c] == dummyPoly:  #this is the first tract found for this county\n",
    "            countyGeom[c] = tractGeom[t]  #seed the county geometry polygon\n",
    "            countyTractList[c] = [t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "            countyTractList[c].append(t)\n",
    "countyMAP = []\n",
    "for c in range(nCounties):  #now build the state map from the counties\n",
    "    plotPoly(countyGeom[c])\n",
    "    if countyMAP == []:  #is this the first county we are adding to map?\n",
    "        countyMAP = countyGeom[c] \n",
    "    else:\n",
    "        countyMAP = countyMAP.union(countyGeom[c])\n",
    "plotPoly(countyMAP)\n",
    "plt.show()\n",
    "print(\"the total of all county pops is\",np.sum(countyPop),\"vs a state pop of\",statePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5bd82dc8-949d-4ba0-8fbe-d42738878d02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets see if countyMAP is a multipolygon.  If so, we will take the biggest piece as the true MAP\n",
      "Nope, it's a vanilla polygon.  stick with original county map\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets see if countyMAP is a multipolygon.  If so, we will take the biggest piece as the true MAP\")\n",
    "finalMAP = countyMAP\n",
    "if countyMAP.type == \"MultiPolygon\":\n",
    "    print(\"by golly, it is!  I'll now plot the main piece of the map\")\n",
    "    maxArea = 0.\n",
    "    finalMAP = dummyPoly\n",
    "    for geom in countyMAP.geoms:\n",
    "        if geom.area > maxArea:\n",
    "            maxArea = geom.area\n",
    "            finalMAP = geom\n",
    "else:\n",
    "    print(\"Nope, it's a vanilla polygon.  stick with original county map\")\n",
    "plotPoly(finalMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3188be73-6288-44dc-bdc6-3c39d19fac6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOT YET IMPLEMENTED - A WAY TO REDUCE MAX RADIUS OF WEDGES THAT WOULD CUT ACROSS CONCAVE MAP AREAS\n",
    "\n",
    "#print(\"I will now define the map's convex hull and split it in fragments\")\n",
    "#print(\"This will help us avoid creating discontinuous wedge pieces\")\n",
    "#MAPbelt = finalMAP.convex_hull().difference(finalMAP)\n",
    "#plotPoly(MAPbelt)\n",
    "#stateCenter = finalMAP.centroid\n",
    "#nRadials = 10  #arbitrary number of    #STILL NEED TO WORK ON THIS SECTION .......\n",
    "#MAPradials = [dummyPoly]*...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5f3c139-e486-473a-8d97-1b00ec4f3430",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now build each precinct's list of neighbors, about 4 min for 9000 precincts\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'countyGeom' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/29098979.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnCounties\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m                 \u001b[0mneighborCountyList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                 \u001b[0mneighborCountyList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'countyGeom' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"I will now build each precinct's list of neighbors, about 4 min for 9000 precincts\")      \n",
    "# start with county neighbor list for efficiency\n",
    "neighborCountyList = [-999]*nCounties\n",
    "for c in range(nCounties):\n",
    "    neighborCountyList[c] = []\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1,nCounties):\n",
    "        if c != cc :  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"done with county neighbors\")\n",
    "\n",
    "neighborList = [\"empty\"]*nTracts\n",
    "for p in range(nTracts):  #this loop takes about 3 minutes for 9000 tracts\n",
    "    neighborList[p] = []\n",
    "for p in range(nTracts):\n",
    "    if p%400 == 0:\n",
    "        print(\"working on census vtd no\",p)\n",
    "    for pp in countyTractList[countyNo[p]] : #for efficiency, look only in home county and ...\n",
    "        if tractGeom[p].intersects(tractGeom[pp]) and p != pp:\n",
    "            neighborList[p].append(pp)\n",
    "    for cc in neighborCountyList[countyNo[p]]:  #... and neighboring counties\n",
    "        for pp in countyTractList[cc]:\n",
    "            if tractGeom[p].intersects(tractGeom[pp]):\n",
    "                neighborList[p].append(pp)\n",
    "print(\"all done finding each county's and each vtd's neighbors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f621b6b-6658-4537-a133-0514c0b78c5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will manually add some neighbors to these\n"
     ]
    }
   ],
   "source": [
    "#SKIP BELOW FOR COLORADO - do not expect any tracts without neighbors *****  OHIO leftover\n",
    "print(\"lets make sure we have enough neighbors for the island precincts\")\n",
    "for p in range(nTracts):\n",
    "    nList = neighborList[p]\n",
    "    isSurroundedTract = \"no\"\n",
    "    if len(nList) == 1:  #checking if this tract is surrounded by a tract.  If so, a buffering will contain it\n",
    "        AREA = tractGeom[p].area\n",
    "        buffPoly = tractGeom[nList[0]].buffer(0.8*AREA**0.5)  #0.8 is conservative; will miss some surrounded ones\n",
    "        if buffPoly.contains(tractGeom[p]) :\n",
    "            isSurroundedTract = \"yes\"\n",
    "    if len(nList) < 2 and isSurroundedTract == \"no\" :\n",
    "            print(\"We have a lonely precinct no\",p,\"; its neighborList is\",neighborList[p])\n",
    "            plotPoly(tractGeom[p])\n",
    "            plotCenter(p,tractGeom[p])\n",
    "            for pp in neighborList[p] :\n",
    "                plotPoly(tractGeom[pp])\n",
    "            plt.show()\n",
    "printList = []\n",
    "for t in range(nTracts):\n",
    "    if vtdCPx[t] <-82.7 and vtdCPx[t] > -82.85 and vtdCPy[t] > 41.35 and t not in skipList:\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t])\n",
    "        printList.append(t)\n",
    "plt.show()\n",
    "print(printList)\n",
    "for p in printList:\n",
    "    print(p,neighborList[p])\n",
    "    \n",
    "print(\"I will manually add some neighbors to these\")\n",
    "neighborList[926].append(1006)\n",
    "neighborList[933].append(3750)\n",
    "neighborList[988].append(3619)  #Hey, this ain't Tampa Bay\n",
    "neighborList[1006].append(926)\n",
    "neighborList[3619].append(988)\n",
    "neighborList[3750].append(933)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ac1e23c5-2f0b-466e-8179-dfcf36acf390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now figure out which tracts are on the map boundary.  About 2min to run\n",
      "working on tract 0\n",
      "working on tract 1000\n",
      "working on tract 2000\n",
      "working on tract 3000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now figure out which tracts are on the map boundary.  About 2min to run\")\n",
    "isMapBoundaryTract = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if tractGeom[t].intersects(finalMAP.exterior):\n",
    "        plotPoly(tractGeom[t])\n",
    "        isMapBoundaryTract[t] = 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c5e3125-081b-4b38-b0a2-1e99a2ec50bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ebc39678-476d-4443-9514-b7be9762ef8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now add in some no-cross areas to stop wedges from picking up discontiguous areas near concavities\n",
      "noCrossPoly-map intersection area = 0.0 for nCP 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAD4CAYAAAAaT9YAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYBUlEQVR4nO3deZRU5ZnH8e/T3SwCskmz2UCDSNQYg0o0KgZESaISTGKSwVHHRA1KMo7LmTjx5MycycRzJsfJTFyyKNEYxyVGY5yoiTEKgrumAVERlX1xgUZEUPbuZ/64t5sGuumqrrp170v9PufU6Vu3bt36eS2efvu996kyd0dERMJUkXYAERHpOBVxEZGAqYiLiARMRVxEJGAq4iIiAasq5Yv169fPa2trS/mSIiLBmzNnzjp3r27tsZIW8draWurq6kr5kiIiwTOzFW09pukUEZGAqYiLiARMRVxEJGAq4iIiAVMRFxEJmIq4iEjAVMRFRAKmIl5G6pav55u3v8TS+o/SjiIiRVLSZh9J19dufh6A9z6cy6OXn4yZpZxIRAqV80jczCrNbJ6ZPdJi3WVm9qaZLTCz65KJKMXyvS98AoA33tvEi8vWp5xGRIohn+mUy4GFTXfM7BTgLOAod/8k8JMiZ5MimzbuEIb0PQCA6U8tTTmNiBRDTkXczGqAM4FbW6yeBvzY3bcBuPva4seTYqqoMC475VAAZr6xlsVrN6WcSEQKletI/HrgaqCxxbpRwMlm9qKZzTazz7T2RDObamZ1ZlZXX19fWFop2FlHD6aqIpoLv/XpZSmnEZFCtVvEzWwSsNbd5+zxUBXQB/gs8D3gPmvlTJm7T3f3Me4+prq61U9SlBLqUlXJlRNHAXDv31axdtPWlBOJSCFyGYmfBEw2s+XAvcAEM7sLWA38wSMvEY3S+yWWVIrm3OOHNi/f+Xybn3ApIgFot4i7+zXuXuPutcAUYKa7nwf8HzABwMxGAZ2BdclFlWLp3a0z3zyxFoCbZi5m8/ad6QYSkQ4rpNnn18AIM3uNaIR+gbt7cWJJ0i4aO7x5+f661SkmEZFC5FXE3X2Wu0+Kl7e7+3nufqS7H+PuM5OJKEkY0rcbZx41CIAbZiyioVG/f0VCpLb7Mjb15BEArP94O48teC/lNCLSESriZezTQ3ozZlgfAH4xazGaDRMJj4p4mbt03CEAvPb2RupWfJByGhHJl4p4mZtwWH8G9+oKwC2z1YovEhoV8TJXUWFcdmrUiv/EwjUs0cfUigRFRVz4ytEHNy/f9oxa8UVCoiIudO1UyVVxK/49L65k3UfbUk4kIrlSERcAzvvssOZlteKLhENFXADo270z58eF/IYZi9iyvSHlRCKSCxVxaXbxybta8R+Yq1Z8kRCoiEuzYQd154ufHAioFV8kFCrispup46JW/PpN23j89TUppxGR9qiIy26OGdqH0UN6A/DL2UvSDSMi7VIRl71MGx+14s9ftYE5K9annEZE9kVFXPZy2uEDGNCzCwDTn1IrvkiWqYjLXiorjMsmRK34jy1Yw/J1H6ecSETaoiIurTr7mJrmZbXii2SXiri06oDOlVwefzDWnS+sYP3H21NOJCKtURGXNp1/wq5W/LteUCu+SBapiEub+vXowt8fPxSA/3n8LbbuUCu+SNaoiMs+XTR2Vyv+g/PeTjGJiLRGRVz26ZDqHkw8YgAAN81YRKNa8UUyRUVc2nXJ56JW/Hc+3MrMN9amnEZEWlIRl3YdO6wPR9X0AtSKL5I1KuLSLjNj2rioFX/Oig+Yt/KDlBOJSBMVccnJ5z85kH49OgPwq6fVii+SFSrikpPKCuOf4uafP7/6Hivf35xyIhEBFXHJw9eO3dWK/+tn1YovkgUq4pKzbp2ruGzCSAB+89xyNmxWK75I2lTEJS//cEJt8/LdL65ML4iIACrikqfqA7sw5TNDgKgVf9tOteKLpElFXPJ28clRK35Do/PHee+knEakvKmIS95G9j+QCYf1B+CmJ9WKL5ImFXHpkKlxK/6q9VuY/VZ9ymlEylfORdzMKs1snpk9ssf6fzYzN7N+xY8nWXX88L4cPqgnADerFV8kNfmMxC8HFrZcYWZDgImALlMoM2bGd8ZHrfgvLlvPq6s/TDmRSHnKqYibWQ1wJnDrHg/9FLga0KRoGTr9yIH07tYJgOlqxRdJRa4j8euJinVj0wozmwy87e7z9/VEM5tqZnVmVldfr7nT/UlVZUXz93A+PP8dVq1XK75IqbVbxM1sErDW3ee0WNcN+AHwb+09392nu/sYdx9TXV1dUFjJnm+MGdK8fPuzy9MLIlKmchmJnwRMNrPlwL3ABOBOYDgwP15fA8w1s4EJ5ZSM6t6lqnlu/NfPLuPDzTtSTiRSXtot4u5+jbvXuHstMAWY6e5nu3t/d6+N168GjnH395KNK1n0zRNrm5fveUnnuEVKSdeJS8H69+zK1+NPOPzpE2+xfWdjO88QkWLJq4i7+yx3n9TK+lp3X1e8WBKab8fNP9t3NvLQfLXii5SKRuJSFKMGHMi4UdGJ658/uRh3XXUqUgoq4lI0l8Sj8WXrPuapRfrDTKQUVMSlaE445CBGDegBwC1qxRcpCRVxKRoz47unRN/889yS91nwjlrxRZKmIi5FdcanBtGjSxUAtz6t7+EUSZqKuBRVp8oKrjgtasV/cN7bvLNhS8qJRPZvKuJSdFOOG9q8fPuzGo2LJElFXIquR5cqLhkXXanyq6eXsXGrWvFFkqIiLon41onDm5fvVSu+SGJUxCURA3t15avHHAzATx9fpFZ8kYSoiEtivn1yNKWyZUcDf3pVrfgiSVARl8QcPqgnY0dGX736s5lqxRdJgoq4JKrpBOeS+o95dvH7KacR2f+oiEuixo7sx4jq7gDc8pRa8UWKTUVcEmVm/GPciv/0onUsfHdjyolE9i8q4pK4SUcNpmun6K2mVnyR4lIRl8R1rqrgytNGAfDA3NW89+HWlBOJ7D9UxKUkzjl+Vyv+b55bnl4Qkf2MiriURM+unbh4bNTFefPsJXy0bWfKiUT2DyriUjIXjlUrvkixqYhLyQzufQBfHj0YgBueWMSOBrXiixRKRVxK6uK4FX/Ttp38+dV3U04jEj4VcSmpIw/uxQkjDgLgF08uUSu+SIFUxKXkmlrx31yzieeXqhVfpBAq4lJy40ZVU3tQNwCmP7U05TQiYVMRl5IzM74bt+LPerOet9ZsSjmRSLhUxCUVk0cPpnNlUyu+RuMiHaUiLqnoUlXJFRMPBeC+utWs3ahWfJGOUBGX1Jx73LDm5TueX55eEJGAqYhLanp168S3TqoF4OdPLuFjteKL5E1FXFJ14Um7WvHvq1uVYhKRMKmIS6qG9O3GpKMGAXDDjEXsVCu+SF5UxCV1Uz8XNf9s2LyDvyx4L+U0ImFREZfUHVXTm+Nq+wJqxRfJV85F3MwqzWyemT0S3/8vM3vDzF4xswfNrHdiKWW/d+n4aDT++rsbeWnZ+pTTiIQjn5H45cDCFvcfB45096OAt4BrihlMysv4Uf2p6XMAoFZ8kXzkVMTNrAY4E7i1aZ27/9Xdm64JewGoKX48KRcVFcZlE6JW/BlvrGXx2o9STiQShlxH4tcDVwNtXTpwIfBoaw+Y2VQzqzOzuvr6+vwTStk4a/TBVFi0fNszGo2L5KLdIm5mk4C17j6njcd/AOwE7m7tcXef7u5j3H1MdXV1QWFl/9a1UyVXTRwFwG9fWkX9pm0pJxLJvlxG4icBk81sOXAvMMHM7gIwswuAScC5rksKpAjOPX5XK/6dasUXaVe7Rdzdr3H3GnevBaYAM939PDP7IvAvwGR335xwTikTfbp35oITokJ+48zFbNnekHIikWwr5DrxnwEHAo+b2ctmdnORMkmZu3Dsrlb8389RK77IvuRVxN19lrtPipdHuvsQdx8d3y5NJqKUm2EHdef0IwcCcMOMxTQ0aqZOpC3q2JRMamrFX/fRNv6qVnyRNqmISyYdPbQPxwztDcAvZ6sVX6QtKuKSWdPGR80/r6z+kDkrPkg5jUg2qYhLZp16WH8G9eoKwC1qxRdplYq4ZFbUih99D+fjr69hab1a8UX2pCIumfbVYw5uXr7tmWUpJhHJJhVxybSunSq58rSoFf/uF1fy/kdqxRdpSUVcMu/8E1q04r+wIsUkItmjIi6Z17d7Z8777FAArn9iEVt3qBVfpImKuAThorEjmpcfmLs6xSQi2aIiLkEY3q87nz9iAAA3zlhEo1rxRQAVcQnIJeOi0fiajdt4YuGalNOIZIOKuATj2GF9+XRNLwBunr0k5TQi2aAiLkGZNv4QAOau3MDclWrFFwmniDc2QmNDDrfGfd/c279JZk08YiD9enQBYPpsteKLVKUdICdbNsCNo2HL/jDysjw3z2f7UPed+/4rgRccdnZphMXg11bm8Mw8siSUO+h95715qO/DBPf9idNh8k357T9HYRTxze9HBfzwyTDwU21vl9Moup1t8hqJ5zlqz3uUn8f2ZbRva3QWvbuR7p2rGFHdvaj7zn3TBP9iSzJ33vvXvgve95InYcVz+T0nD2EU8SaHfwmO+kbaKSRllcA+fpWLZMvvL4R35ye2+3DmxEVEZC8q4iIiAVMRFxEJmIq4iEjSEjwRriIuIpIoI+8rWvIQRhFXA46IhCrva/jzE0YRFxEJmaZTmiT7G01EpPg0nSIiEi4zjcRFRMKlOXERkcBpJC4iEiazJGu4iriISLJ0YlNEJFy6ThwS/VtERCRpujollvBvNBGR4svIdIqZVZrZPDN7JL7f18weN7NF8c8+iaUUEQlVwmPPfEbilwMLW9z/PjDD3Q8FZsT3RURkT2lPp5hZDXAmcGuL1WcBd8TLdwBfLmoyEZH9QjamU64HrgYaW6wb4O7vAsQ/+7f2RDObamZ1ZlZXX19fSFYRkfCk3XZvZpOAte4+pyMv4O7T3X2Mu4+prq7uyC5ERAKW7KR4Lt92fxIw2czOALoCPc3sLmCNmQ1y93fNbBCwNsmgIiLhSnEk7u7XuHuNu9cCU4CZ7n4e8BBwQbzZBcAfE0upL4UQkVClPZ2yDz8GJprZImBifD8hKuIiEqpkT2zmMp3SzN1nAbPi5feBU4sfaR/U7CMioVHbPZpOEZGwZXQ6pYSaDoBG4iISmmxcJ54Nmk4RkdB8uBo2v5/Y7sMo4ppOEZFQLXos0d2HUcQ1nSIioRo+LtHdh1HEm0bimk4RkdAMOBI690hs92EU8WYq4iISmIoKaGxIbveJ7bmoNCcuIoGySvByL+Ief3hiRWW6OURE8lVRqZF48wGwMOKKiDTTSJwWJzY1EheRwFTEn27S2Ljv7Tq6+0T2WmxN0ykaiYtIaCriutW4M5ndJ7LXYmv6U0SXGIpIaJpmEBKaUgmkiOvEpogEqqluJXRyM6wirukUEQmNRuLo6hQRCZdG4sDObdHPqq7p5hARyVfT4DOhD/ILo4hv2xj9TPDzB0REEtE8g1DORXz7R9HPLgemm0NEpKO8nK8T39ZUxDUSF5HAaDqFXSNxTaeISGiai3g5j8QbdkSX6eg6cREJTVOTYlkXcW9Ut6aIhEknNgFc14iLSJg0nUI8Eg8jqojI7jSdoiIuIuHSSJzo0hwVcREJUfOJzXKeE3dHX5IsImFKtnYFUsQ1nSIigVKzD7rEUETCpevEUREXkf1AOY/Eew6GLR/ApjVpJxERyY+mU4CRp0Y/l8xMN4eISL40nQIM+BR06wfLnko7iYhInpqmglMaiZtZVzN7yczmm9kCM/thvH60mb1gZi+bWZ2ZHZdIQoCKCuheDds3JfYSIiKJSPg68aocttkGTHD3j8ysE/CMmT0K/AfwQ3d/1MzOAK4DxieSEqJ5pYQOgohIYhL+AKx2i7i7OxB/oDed4pvHt57x+l7AO0kEbGamIi4iAUp2TjyXkThmVgnMAUYCP3f3F83sCuAxM/sJ0bTMiW08dyowFWDo0KEdT2pGUr/JREQSk4W2e3dvcPfRQA1wnJkdCUwDrnT3IcCVwG1tPHe6u49x9zHV1dUFRLXEfpOJiCQmS58n7u4bgFnAF4ELgD/ED90PJHdiE+I5cRVxEQlNypcYmlm1mfWOlw8ATgPeIJoDHxdvNgFYlEjCXUES3b2ISCKap1OS2X0uc+KDgDviefEK4D53f8TMNgA3mFkVsJV43ltERFpK9jrxXK5OeQU4upX1zwDHJhFKRERyE0bHpohIqLJwdUpm6DpxEQmOvhQi0tgAFZVppxAR6aByH4l7I5iKuIgEpvm8ZrkX8caG6IOwRESCoumUiDdARU6fEiAikkHlPhLXlyWLSIh0dUqssUFFXERkD+FURY3ERSRIKX+zT2a46+oUEQmPplNirukUEZE9hVMVvVGfZCgiAdJ0SsQb1bEpIuHRdEpMV6eIiOwlnO4Zb4SG7bB5/d6PtTrN0sbUS67bFnu7kr12AvvUNJZIAVL+PPHMqKiEeXdFNymt3kOholOKATLw6ZWpf4JmHq+fV9Yct83rPz/lrHnvt4XmAYvtcb/luj3vt7ZNi/3s2FJYpnaEU8TPvg3WLmzlgVYOTKsHq4DtQtlnm++RAva5Ywt8uIqkP/+hXZn4ayCkY5DHtjnvN4l95rvfPHab9/8v3+3H7uucvR7cc92+tuncHQaPzjNPbsIp4iPGRTcREWmmM4UiIgFTERcRCZiKuIhIwFTERUQCpiIuIhIwFXERkYCpiIuIBExFXEQkYOYlbCc2s3pgRRF21Q9YV4T9lEJIWSGsvMqanJDylkPWYe5e3doDJS3ixWJmde4+Ju0cuQgpK4SVV1mTE1Lecs+q6RQRkYCpiIuIBCzUIj497QB5CCkrhJVXWZMTUt6yzhrknLiIiERCHYmLiAgq4iIiQct0ETezr5vZAjNrNLMxezx2jZktNrM3zewLLdb/nZm9Ej/vugDynmNmr8aZ/2Jm/bKY1cwONLOXW9zWmdn1Wcwar+9sZtPN7C0ze8PMzi5F1gLyzorXNR3f/lnN2uLxh8zstVLk7GjW+N/U/Ph5N5tZZVbzmlk3M/tT/H5dYGY/zumF3D2zN+Bw4BPALGBMi/VHAPOBLsBwYAlQCRwErASq4+3uAE7NcN4qYC3QL97uOuDfs5i1lefPAT6X1azAD4Fr4+WKpmOc4by7bZvlrPHjXwXuAV7LclagZ/zTgAeAKVnNC3QDTom36Qw8DZze3utkeiTu7gvd/c1WHjoLuNfdt7n7MmAxcBwwAnjL3evj7Z4ASjYC60Bei2/dzcyAnsA7Gc3azMwOBfoTvckS18GsFwL/GT+/0d1L1tFXyLEttY5kNbMewFXAtaVL2rGs7r4x3qaKqDCW7EqOfPO6+2Z3fzJ+7nZgLlDT3utkuojvw8HAqhb3V8frFgOHmVmtmVUBXwaGlD7eXlrN6+47gGnAq0TF+wjgttLH201bx7alc4DfeTxkSFGrWc2sd3z/R2Y218zuN7MBJU+3t/aO7e3xVMq/xr/U07SvrD8C/hvYXOpQbdjncTWzx4j+4t0E/L600VrV7r+x+D38JWBGeztL/YuSzewJYGArD/3A3f/Y1tNaWefu/oGZTQN+BzQCzxGNzoummHnNrBNRET8aWArcBFxDkUY4xcy6x/0pwPmFZNvrRYubtYpoBPOsu19lZlcBP6GImRM4tue6+9tmdiDRn/3nA/9beNKiv2dHAyPd/Uozqy1Gvt1eNIH3rLt/wcy6AncDE4DHCw7a9MIJ5I0HoL8FbnT3pe1lSL2Iu/tpHXjaanYfYdcQT0O4+8PAwwBmNhVoKDRjS0XOOzre5xIAM7sP+H6BEZsV+9gCmNmngSp3n1NgvN0UOev7RKPEB+P19wMXFRRwDwm8b9+Of24ys3uIpgOKUsSLnPUE4FgzW05UP/qb2Sx3H19oTkjmPRvvd6uZPUQ0lVG0Ip5Q3unAIne/PpedhTqd8hAwxcy6mNlw4FDgJYCms/pm1gf4DnBrail3aSvv28ARZtb06WQTgYUpZWzS5rGNnUM0SsiCVrPG0zwPA+Pj7U4FXk8n4m5azWtmVRZflRT/dTYJKNlVH21o69j+0t0Hu3stMJboHNT4FHNC28e1h5kNgubR7RnAGynmbLKv+nUt0Au4Iue9lepMbUduwFeIfmttA9YAj7V47AdEZ3XfpMUZXKIC83p8K9mZ6ALyXkpUuF8hKjwHZTVr/NhS4LAAjusw4Kn4uM4AhmY1L9Cd6GqfV4AFwA20ckVQFrLu8dxaSnt1Sr7HdQDwtxbH9SaivyKzmreGaFplIfByfLu4vddR272ISMBCnU4RERFUxEVEgqYiLiISMBVxEZGAqYiLiARMRVxEJGAq4iIiAft/4h424RkUKOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now add in some no-cross areas to stop wedges from picking up discontiguous areas near concavities\")\n",
    "maxR = finalMAP.area ** 0.5\n",
    "starterPoints = [(Point(-106,41.01))]  #[ Point(-83., 38.7), Point(-82.1, 38.9), Point(-81.6,39.2), Point(-82.5,41.4) ]\n",
    "mapCenter = finalMAP.centroid\n",
    "nNoCrosses = len(starterPoints)\n",
    "noCrossPolyList = [dummyPoly]*nNoCrosses\n",
    "sP = 0\n",
    "for starterPoint in starterPoints:\n",
    "    centerDistance = mapCenter.distance(starterPoint)\n",
    "    farPoint = Point(starterPoint.x + maxR*(starterPoint.x - mapCenter.x)/centerDistance,\n",
    "                     starterPoint.y + maxR*(starterPoint.y - mapCenter.y)/centerDistance)\n",
    "    noCrossPolyList[sP] = LineString([starterPoint,farPoint]).buffer(0.01)\n",
    "    plotPoly(noCrossPolyList[sP])\n",
    "    print(\"noCrossPoly-map intersection area =\",noCrossPolyList[sP].intersection(finalMAP).area,\"for nCP\",sP)\n",
    "    sP +=1\n",
    "plotPoly(finalMAP)\n",
    "plt.show()\n",
    "# NEED TO ADD HERE FOR CONCAVE STATES - A LIST OF noCross POLY'S: IF BOTH RADII OF A WEDGE CROSS THE SAME noCross, THEN\n",
    "# REDUCE wR FROM maxR TO THE MAXIMUM OF THE DISTANCE FROM TRACTCP TO THE TWO INTERSECTION POINTS\n",
    "# THE noCross POLY'S CAN BE CREATED FROM MAP BUFFER - MAP, DUPLICATED, SPLIT INTO EIGHT 90-DEG SECTIONS\n",
    "\n",
    "def crossCheck(cp, ANGLE, ANGLE2, maxR, noCrossPolyList):  #if a triangle's two radial edges cross a no-land, shorten maxR \n",
    "    result = maxR  #default, no barred crossing found\n",
    "    PT1 = Point(cp.x+maxR*math.cos(ANGLE),  cp.y+maxR*math.sin(ANGLE) )\n",
    "    PT2 = Point(cp.x+maxR*math.cos(ANGLE2), cp.y+maxR*math.sin(ANGLE2) )\n",
    "    line1 = LineString([cp, PT1])\n",
    "    line2 = LineString([cp, PT2])\n",
    "    for nCP in noCrossPolyList:\n",
    "        if line1.intersects(nCP) and line2.intersects(nCP):\n",
    "            dist1 = cp.distance(line1.intersection(nCP))\n",
    "            dist2 = cp.distance(line2.intersection(nCP))\n",
    "            if max(dist1,dist2) < result:\n",
    "                result = max(dist1, dist2)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f762e8b4-c357-4a57-8cc8-d408212dc172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confirming our county (first plot) and tract neighbor list (2nd plot) in histograms\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"confirming our county (first plot) and tract neighbor list (2nd plot) in histograms\")\n",
    "nB = 20\n",
    "nCountyNeighbors = [0]*nCounties\n",
    "nTractNeighbors = [0]*nTracts\n",
    "for c in range(nCounties):\n",
    "    nCountyNeighbors[c]=len(neighborCountyList[c])\n",
    "for t in range(nTracts):\n",
    "    nTractNeighbors[t] = len(neighborList[t])\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(nCountyNeighbors,bins=[0,1,2,3,4,5,6,7,8,9,10])\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(nTractNeighbors,bins=[0,1,2,3,4,5,7,10,13,17,22,30])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "05e892dc-783b-4e88-aa0e-011f0df938a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ready to draw 3108 Home Districts for a 8 -district CO map ( 721714.25 per district).  Map diagonal =  8.08393\n"
     ]
    }
   ],
   "source": [
    "MAP = finalMAP\n",
    "nDistricts = 8   #CO congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "MAPdiagonal = ((MAPMaxY - MAPMinY)**2 + (MAPMaxX - MAPMinX)**2)**0.5\n",
    "maxR = MAPdiagonal\n",
    "MAPboundary = MAP.exterior\n",
    "print(\"ready to draw\",nTracts,\"Home Districts for a\",nDistricts,\"-district\",STATE,\n",
    "      \"map (\",r3(avgDistrictPop),\"per district).  Map diagonal = \",r5(maxR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3ee429df-90fd-4872-abe2-0ea6c0db9573",
   "metadata": {},
   "outputs": [],
   "source": [
    "def r3L(LIST):  #shortcut for debug-print of a list\n",
    "    result = \"[\"\n",
    "    for L in LIST:\n",
    "        result = result + str(round(L,2)) +\", \"\n",
    "    result = result+\"]\"\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79295824-6468-46c4-a6d7-09fef6426e07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let me display a list of tracts that had convergence problems\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "problemTractList = [2332, 2364, 2389, 2392, 2393, 2394]\n",
    "print(\"Let me display a list of tracts that had convergence problems\") #these needed me to increase maxR for wide angles\n",
    "for p in problemTractList:\n",
    "    plotPoly(tractGeom[p])\n",
    "    plotCenter(p,tractGeom[p])\n",
    "    plotPoly(countyGeom[countyNo[p]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31fe4f5f-8686-4901-acda-503ce59c6f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Lets write the tract Biden and Trump pops and neighbor lists to a file before we start\")\n",
    "date = \"14Sep\"\n",
    "tractNo = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"tractPop\":tractPop,\n",
    "                   \"vtdBiden\":vtdBiden,\"vtdTrump\":vtdTrump,\"neighborList\":neighborList} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"vtd_input_data.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "85a810e1-a853-4c1e-b2b0-16a2670c4017",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For each Home District, we will consider a wedge as full if it is within 8074.0 of targetWedgePop\n",
      "I am working on tract number 0 of 3108 tracts\n",
      "I am working on tract number 100 of 3108 tracts\n",
      "I am working on tract number 200 of 3108 tracts\n",
      "I am working on tract number 300 of 3108 tracts\n",
      "I am working on tract number 400 of 3108 tracts\n",
      "I am working on tract number 500 of 3108 tracts\n",
      "I am working on tract number 600 of 3108 tracts\n",
      "I am working on tract number 700 of 3108 tracts\n",
      "I am working on tract number 800 of 3108 tracts\n",
      "I am working on tract number 900 of 3108 tracts\n",
      "I am working on tract number 1000 of 3108 tracts\n",
      "I am working on tract number 1100 of 3108 tracts\n",
      "I am working on tract number 1200 of 3108 tracts\n",
      "I am working on tract number 1300 of 3108 tracts\n",
      "I am working on tract number 1400 of 3108 tracts\n",
      "I am working on tract number 1500 of 3108 tracts\n",
      "I am working on tract number 1600 of 3108 tracts\n",
      "I am working on tract number 1700 of 3108 tracts\n",
      "I am working on tract number 1800 of 3108 tracts\n",
      "I am working on tract number 1900 of 3108 tracts\n",
      "I am working on tract number 2000 of 3108 tracts\n",
      "I am working on tract number 2100 of 3108 tracts\n",
      "I am working on tract number 2200 of 3108 tracts\n",
      "I am working on tract number 2300 of 3108 tracts\n",
      "I am working on tract number 2400 of 3108 tracts\n",
      "I am working on tract number 2500 of 3108 tracts\n",
      "I am working on tract number 2600 of 3108 tracts\n",
      "I am working on tract number 2700 of 3108 tracts\n",
      "I am working on tract number 2800 of 3108 tracts\n",
      "I am working on tract number 2900 of 3108 tracts\n",
      "I am working on tract number 3000 of 3108 tracts\n",
      "I am working on tract number 3100 of 3108 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0\n",
      "Average and max number of wedgePop loops per tract were:  10.213963963963964 19\n",
      "calculated statewide vote was 0.4325078902010655, should have been 0.4306077428272965\n",
      "calcd statewide Hispanic pop was 0.1899249560439887, should have been 0.1916357546696186\n",
      "calcd statewide Black pop was 0.03764488871899738, should have been 0.038895009198204\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.10263835263835264\n"
     ]
    }
   ],
   "source": [
    "#9/5/22 - this is a hybrid of HD2 and the organic code.  We build wedges, but tract inclusion is binary except for final tract to meet aDP\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point.  Orient the 0th wedge to face this point\n",
    "# ... determine the wedgePop for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add a slice of the nearest non-included tract to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  compute the HD's R/D lean and ethnic demographics.  Compute tractUse across all HDs at the end from the tractUsageList s\n",
    "#minTractPop = 10  #not needed for vtd-based geom's; all will be populated\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "wedgePoly = [dummyPoly]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop for worst wedge\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [[]]*nTracts\n",
    "partialTract = [-999]*nTracts  #at most one tract in a drawn Home District will be only partially included\n",
    "partialTractUse = [0.]*nTracts  #and this is what fraction of the tract is included (we prorate voters and tractPop)\n",
    "stateGOP = np.sum(vtdTrump) / ( np.sum(vtdBiden) + np.sum(vtdTrump) )\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDtotVote = [0.]*nTracts  #total Biden + Trump voters in this home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDPoly1 = [dummyPoly]*nTracts  #final 4-wedge shape, NOT trimmed to state boundary\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle?? starting ANGLE ?? for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "tolerPop = 0.5 * np.max(tractPop)  #absolute slop in districtPop before adding a partial tract to finish it\n",
    "print(\"For each Home District, we will consider a wedge as full if it is within\",tolerPop,\"of targetWedgePop\")\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "tractPrintInterval = 100  #for tracking progress\n",
    "isNonEquiAngle = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW for \"HD2\" 3/7/22\n",
    "maxAngle = 1.8*pi/2.  #NEW 9/11/22 - AVOID super-wide angles very close to boundary\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "avgTractPop = np.sum(tractPop)/nTracts\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "partialTractNo = [-999]*nTracts #this will be the tract no for an HD's partially used tract on its boundary\n",
    "wedgeTractList = [[]]*nWedges\n",
    "printDebug = \"no\"\n",
    "\n",
    "for t in range(nTracts):  #populatedTractList:  #range(nTracts) : #loop on each tract.  \n",
    "    HDweight[t] = tractPop[t]/np.sum(tractPop) #Start by resetting stats\n",
    "    #tractCPball = tractCP[t].buffer(0.001)  #obsolete; was used to smooth HDpoly\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge  #ADD THIS BACK IN\n",
    "    #angle0[t] = 0.\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS ----------------\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    angle[0] = angle0[t] - 0.5*wedgeAngle[0]\n",
    "    for nW in range(nWedges) :  #this loop: compute the max wedgePops with this initial random starter angle\n",
    "        maxWedgePop[nW] = tractPop[t]*wedgeAngle[nW]/(2.*pi) #seed w/ fractn of starter tract (to be excluded below)\n",
    "        if nW > 0:\n",
    "            angle[nW] = angle2[nW-1]            #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]   #local angle for clockwise END of wedge\n",
    "        wR = crossCheck(tractCP[t], angle[nW],angle2[nW],maxR,noCrossPolyList)  #reduce from global max in concave areas\n",
    "        pt1[nW] = tractCP[t]\n",
    "        pt2[nW] = Point(tractCP[t].x+wR*math.cos(angle[nW]),  tractCP[t].y+wR*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP[t].x+wR*math.cos(angle2[nW]), tractCP[t].y+wR*math.sin(angle2[nW]) )\n",
    "        wedgePoly[nW] = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "        c = 0  #below loop will kick out before checking all counties once we know wedgePop can exceed targetWedgePop\n",
    "        while maxWedgePop[nW] < targetWedgePop[nW] and c < nCounties:  #building wedgePop w/infinite radius across all counties\n",
    "            if wedgePoly[nW].intersects(countyGeom[c]):\n",
    "                if wedgePoly[nW].contains(countyGeom[c]):\n",
    "                    maxWedgePop[nW] += countyPop[c]\n",
    "                else:  #partial county overlap\n",
    "                    for tt in countyTractList[c]:\n",
    "                        if wedgePoly[nW].contains(tractCP[tt]) and tt != t :  #we already counted the starter tract'\n",
    "                            maxWedgePop[nW] += tractPop[tt]\n",
    "            if maxWedgePop[nW] >= targetWedgePop[nW] - tolerPop:\n",
    "                willFill[nW] = 1\n",
    "            c +=1   # on to next county if we still have not reached targetWedgePop\n",
    "        #print(\"t,wedge,angle, mWP (tWP) =\",t,nW,r3(90./pi*(angle[nW]+angle2[nW])),r3(maxWedgePop[nW]),r3(targetWedgePop[nW]))\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        #print(\"At least one wedge could not reach its wedgePop target\",targetWedgePop,maxWedgePop)  #debug\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the closest boundary for the shortest wedgePop\n",
    "        shortedPoly = wedgePoly[minW]\n",
    "        edgeLine = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        minDistance = tractCP[t].distance(edgeLine)  #closest distance to edge where wedge hit the boundary\n",
    "        edgeBall = tractCP[t].buffer(1.01*minDistance)  #build a circle a little bigger than this distance to ensure intersxn\n",
    "        edgeArc = edgeBall.intersection(edgeLine)  #only count MAP boundary intersxns within the shortest wedge\n",
    "        if edgeArc.is_empty:  #Can't find an intersection; something is off...  \n",
    "            angle0[t] = ( 0.5*(angle[minW]+angle2[minW]) ) %2.*pi #.. Just set an average angle0 for re-orienting this shortest wedge\n",
    "            \n",
    "        else: # we found an intersection\n",
    "            closePoint = edgeArc.centroid\n",
    "            dx = closePoint.x - tractCPx[t]\n",
    "            dy = closePoint.y - tractCPy[t]\n",
    "            exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "            if (dx != 0. ):\n",
    "                exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                    exitAngle = pi + exitAngle\n",
    "            angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point\n",
    "        \n",
    "        # OK, we've re-oriented the shortest wedge to face the boundary. Need to recompute all wedges' start/stop angles\n",
    "        angle[0] = angle0[t] - 0.5*wedgeAngle[0]   #start of wedge is a half-angle away from avg wedge orientation\n",
    "        #print(\"Our new avg angle for the shortest wedge for tract\",t,\"is\", r3(angle0[t]),\"rad =\",r3(180./pi*angle0[t]),\"deg\")\n",
    "        for nW in range(nWedges):\n",
    "            if nW > 0:\n",
    "                angle[nW] = angle2[nW-1]        #local variable for orientation of START of wedge\n",
    "            angle2[nW] = angle[nW]+wedgeAngle[nW]    #local angle for clockwise END of wedge            \n",
    "            wR = crossCheck(tractCP[t], angle[nW],angle2[nW],maxR,noCrossPolyList)  #reduce from global max in concave areas  \n",
    "            #set each wedge radius to max to find the max possible wedgePop.  Trim radius if in concave area\n",
    "            pt1[nW] = tractCP[t]\n",
    "            pt2[nW] = Point(tractCP[t].x+wR*math.cos(angle[nW]),  tractCP[t].y+wR*math.sin(angle[nW]) )\n",
    "            pt3[nW] = Point(tractCP[t].x+wR*math.cos(angle2[nW]), tractCP[t].y+wR*math.sin(angle2[nW]) )\n",
    "            wedgePoly[nW] = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation\n",
    "        willFill = [0]*nWedges\n",
    "        for nW in range(nWedges):\n",
    "            maxWedgePop[nW] = tractPop[t]*wedgeAngle[nW]/(2.*pi) #seed w/ fractn of starter tract, which will be excluded below\n",
    "            c = 0  \n",
    "            while maxWedgePop[nW] < avgDistrictPop and c < nCounties: #don't use tWP here; may need more out of wedge\n",
    "                if wedgePoly[nW].intersects(countyGeom[c]):\n",
    "                    if wedgePoly[nW].contains(countyGeom[c]):\n",
    "                        maxWedgePop[nW] += countyPop[c]\n",
    "                    else:  #partial county overlap\n",
    "                        for tt in countyTractList[c]:\n",
    "                            if wedgePoly[nW].contains(tractCP[tt]) and tt != t  : \n",
    "                                maxWedgePop[nW] += tractPop[tt]\n",
    "                if maxWedgePop[nW] >= targetWedgePop[nW] - tolerPop:\n",
    "                    willFill[nW] = 1\n",
    "                c +=1   # on to next county if we still have not reached targetWedgePop        \n",
    "            #print(\"t,w,new angle, mWP (tWP) =\",t,nW,r3(90./pi*(angle[nW]+angle2[nW])),maxWedgePop[nW],r3(targetWedgePop[nW]))\n",
    "            \n",
    "    nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "    minW = maxWedgePop.index(np.min(maxWedgePop))  #minimum wedge ...\n",
    "    oppW = int( int(minW + nWedges/2) % nWedges )  #and its opposing wedge\n",
    "\n",
    "    if nUnfilledWedges >= nWedges:\n",
    "        print(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUnfilledWedges == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        Lstar = ( maxWedgePop[minW] / (0.25*avgDistrictPop) )**0.5  #nondim'l distance to boundary \n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUnfilledWedges == 0 or nUnfilledWedges == nWedges - 1:#far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUnfilledWedges == 2:  #must determine if opposite or adjacent           \n",
    "        if maxWedgePop[oppW] < targetWedgePop[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(nWedges):\n",
    "                if willFill[nW] == 0 and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = maxWedgePop[adjW] / targetWedgePop[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebug == \"yes\":\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",maxWedgePop[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "                Lstar = ( maxWedgePop[minW] / (0.25*avgDistrictPop) )**0.5  #nondim'l distance to boundary \n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        targetWedgePop = [avgDistrictPop / float(nWedges)]*nWedges  #reset; will adjust immdtly below for amy shorted pops\n",
    "        orderedMWP = [0.]*nWedges  #this loop: sort by maxWedgePop to pass extra targetWedgePop to those that can take\n",
    "        for nW in range(nWedges):\n",
    "            maxWedgePop[nW] -= random.uniform(0,0.0001)  #to fix rare orderedMWP mis-sort on rare corners\n",
    "            orderedMWP[nW] = maxWedgePop[nW]\n",
    "        orderedMWP.sort() #sorting the wedges by their maxWedgePop\n",
    "        if printDebug == \"yes\" :\n",
    "            print(\"tract, ordered maxWedgePop list is\",t,r3L(orderedMWP))\n",
    "        for nn in range(nWedges):  #going from least to most maxWedgePop here ...\n",
    "            nW = maxWedgePop.index(orderedMWP[nn])\n",
    "            if maxWedgePop[nW] < targetWedgePop[nW]: #need to redistribute extra target to more populous wedges ...\n",
    "                wedgePopGap = targetWedgePop[nW] - maxWedgePop[nW]\n",
    "                targetWedgePop[nW] = maxWedgePop[nW]  #resetting this target to be attainable\n",
    "                nReceivers = 0.\n",
    "                for WW in range(nWedges):\n",
    "                    if maxWedgePop[WW] > targetWedgePop[WW]:  #this wedge could take at least a little more ....\n",
    "                        nReceivers += 1.\n",
    "                for WW in range(nWedges):\n",
    "                    if maxWedgePop[WW] > targetWedgePop[WW]:  #this wedge could take at least a little more ....                    \n",
    "                        targetWedgePop[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap\n",
    "            if printDebug == \"yes\" :\n",
    "                print(\"after real wedge\",nW,\"gap=\",r3(wedgePopGap))\n",
    "                print(\"orig-order tWPs are now\",r3L(targetWedgePop),\n",
    "                      \"which sum to\",r3(np.sum(targetWedgePop)-avgDistrictPop),\"relative to avgDistrictPop\")\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles, recompute maxWPs, and then ...\n",
    "        isNonEquiAngle[t] = 1\n",
    "        if printDebug == \"yes\" :\n",
    "            print(\"t,current list of STARTING angles are\",t,r3L(angle))\n",
    "        adjWedgeList = []  #... assign each wedge's targetPop based on other wedges' maxWPs\n",
    "        minW = 0  #we force the 0th wedge to be the one facing the boundary (it's usually the shortest)\n",
    "        oppW = int(nWedges/2)  \n",
    "        for nW in range(nWedges):\n",
    "            if nW != minW and nW != oppW:\n",
    "                adjWedgeList.append(nW)  #building the list of \"adjacent\" wedges (excl min and opp)\n",
    "        wideAngle = min(maxAngle, avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - Lstar)/(levelL - 0.) )  ) )\n",
    "        wedgeAngle[minW] = wideAngle\n",
    "        wedgeAngle[oppW] = wideAngle\n",
    "        for nW in adjWedgeList: #decrease these angles so sum(angles) = 2 pi\n",
    "                wedgeAngle[nW] = (2.*pi - 2.*wideAngle) / (nWedges - 2.)\n",
    "        #one more restart to identify each (adjusted) wedge's max Pop using our NEW ANGLES\n",
    "        angle[minW] = angle0[t] - 0.5 * wideAngle\n",
    "        angle2[minW] = angle[0] + wideAngle\n",
    "        for ww in range(minW+1,minW+nWedges,1) :\n",
    "            nW = int(ww % 4)\n",
    "            previousWedge = int( (nW+3) % 4)  #protects against minW = nWedges-1\n",
    "            angle[nW] = angle2[previousWedge]\n",
    "            angle2[nW] = angle[nW]+wedgeAngle[nW]\n",
    "        if printDebug == \"yes\" :\n",
    "            print(r3(angle0[t]),\"= final angle0. New list of STARTING angles are\",r3L(angle))\n",
    "            print(\"new list of ENDING angles are\" ,r3L(angle2),\"and minW no =\",minW)\n",
    "        willFill = [0]*nWedges #need to reset, since redrawing with new angles.  =\n",
    "        for nW in range(nWedges) :  #this loop: compute the max wedgePops (last time, I promise) with adjusted angles\n",
    "            cosHA = math.cos(0.5*wedgeAngle[nW])  #cosine of half-angle (needed to extend R for wide angles)\n",
    "            wR = crossCheck(tractCP[t], angle[nW],angle2[nW],maxR/cosHA,noCrossPolyList)  #reduce from global max in concave areas\n",
    "            #wR = maxR/cosHA  #current value of this wedge's radius\n",
    "            pt1[nW] = tractCP[t]\n",
    "            pt2[nW] = Point(tractCP[t].x+wR*math.cos(angle[nW]),  tractCP[t].y+wR*math.sin(angle[nW]) )\n",
    "            pt3[nW] = Point(tractCP[t].x+wR*math.cos(angle2[nW]), tractCP[t].y+wR*math.sin(angle2[nW]) )\n",
    "            wedgePoly[nW] = Polygon([pt1[nW], pt2[nW], pt3[nW] ])  \n",
    "            c = 0  \n",
    "            stopPop = avgDistrictPop   #wedges may need to carry up to aDP if others are constrained ...\n",
    "            if nW == minW :\n",
    "                stopPop = avgDistrictPop / nWedges  #... except the minW can't carry extra\n",
    "            maxWedgePop[nW] = tractPop[t]*wedgeAngle[nW]/(2.*pi)   #need to adjust seed tract pop split due to angle changes\n",
    "            while maxWedgePop[nW] < stopPop and c < nCounties:  #we're unsure of targetWedgePop, since some may be shorted\n",
    "                if wedgePoly[nW].intersects(countyGeom[c]):\n",
    "                    if wedgePoly[nW].contains(countyGeom[c]):\n",
    "                        maxWedgePop[nW] += countyPop[c]\n",
    "                    else:  #partial county overlap\n",
    "                        for tt in countyTractList[c]:\n",
    "                            if wedgePoly[nW].contains(tractCP[tt]) and tt != t : #as always, home tract alrdy counted\n",
    "                                maxWedgePop[nW] += tractPop[tt]\n",
    "                if maxWedgePop[nW] >= targetWedgePop[nW] - tolerPop:\n",
    "                    willFill[nW] = 1\n",
    "                c +=1   # on to next county if we still have not reached targetWedgePop\n",
    "\n",
    "        targetWedgePop[minW] = min(maxWedgePop[minW],avgDistrictPop/nWedges)\n",
    "        targetWedgePop[oppW] = min(maxWedgePop[oppW],targetWedgePop[minW]) #oppW constraint capped by minW\n",
    "        orderedMWP = [0.]*nWedges  #this loop: sort by maxWedgePop to pass extra targetWedgePop to those that can take\n",
    "        for nW in range(nWedges):\n",
    "            maxWedgePop[nW] -= random.uniform(0,0.0001)  #to fix rare orderedMWP mis-sort on rare corners\n",
    "            orderedMWP[nW] = maxWedgePop[nW]\n",
    "            if nW!= minW and nW!= oppW :\n",
    "                targetWedgePop[nW] = (avgDistrictPop - targetWedgePop[minW]-targetWedgePop[oppW]) / (nWedges - 2.)\n",
    "        orderedMWP.sort() #sorting the wedges by their maxWedgePop\n",
    "        if printDebug == \"yes\" :\n",
    "            print(\"t,orig, min-to-max order of maxWedgePops are\",t,r3L(maxWedgePop),r3L(orderedMWP))\n",
    "            print(\"and the original-order targetWedgePops were\",r3L(targetWedgePop) )\n",
    "        for nn in range(nWedges):  #going from least to most maxWedgePop here ...\n",
    "            nW = maxWedgePop.index(orderedMWP[nn])\n",
    "            #print(\"before this jigger on wedge\",nW,\", 0-1-2-3 targetWedgePops were\",r3L(targetWedgePop))\n",
    "            if maxWedgePop[nW] < targetWedgePop[nW]: #need to redistribute extra target to more populous wedges ...\n",
    "                wedgePopGap = targetWedgePop[nW] - maxWedgePop[nW]\n",
    "                targetWedgePop[nW] = maxWedgePop[nW]\n",
    "                nReceivers = 0.\n",
    "                for WW in range(nWedges):\n",
    "                    if maxWedgePop[WW] > targetWedgePop[WW]:  #this wedge could take at least a little more ....\n",
    "                        nReceivers += 1.\n",
    "                for WW in range(nWedges):\n",
    "                    if maxWedgePop[WW] > targetWedgePop[WW]:  #this wedge could take at least a little more ....                    \n",
    "                        targetWedgePop[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap\n",
    "            if printDebug == \"yes\" :\n",
    "                print(\"after true wedge\",nW,\"gap=\",r3(wedgePopGap),\"orig-order tWPs are now\",r3L(targetWedgePop),\n",
    "                      \"which sum to\",r3(np.sum(targetWedgePop)-avgDistrictPop),\"relative to avgDistrictPop\")\n",
    "                \n",
    "        #print(\"constrain opp wedge for\",t,\"Lstar,tWPs are\",Lstar,targetWedgePop)  #DEBUG\n",
    "\n",
    "    #OK, we've now finalized all the wedge angles, all targetWedgePops and max possible wedgePops\n",
    "    # Ready to determine each wedge's final radius to complete its targetPop\n",
    "    HDvPop[t] = 0.\n",
    "    offsetPop = [0.]*nWedges  #how much the current wedgePop is above its targetWedgePop\n",
    "    tractLoopCounter[t] = 0\n",
    "    for nW in range(nWedges):\n",
    "        wedgeTractList[nW] = []\n",
    "        wedgePop[nW] = tractPop[t] * wedgeAngle[nW]/(2.*pi)  #here, reseed w/frac tractPop, whole will not be picked up\n",
    "        wR = 0.001     \n",
    "        dR = 0.5 * maxR / math.cos(0.5*wedgeAngle[nW])  #cosine of half-angle (needed to extend R for wide angles)\n",
    "        offsetPop[nW] = wedgePop[nW] - targetWedgePop[nW]\n",
    "        counter = 0\n",
    "        while abs(offsetPop[nW]) > tolerPop and counter < nLoopPrint:\n",
    "            wedgePop[nW] = tractPop[t] * wedgeAngle[nW]/(2.*pi)\n",
    "            wedgeTractList[nW] = [] #we exclude the home tract from the wedge lists, will add later to final list\n",
    "            counter +=1\n",
    "            wR= wR - dR * np.sign(offsetPop[nW])  #apply bisection step to get new wedge radius\n",
    "            CwR = crossCheck(tractCP[t], angle[nW],angle2[nW],wR,noCrossPolyList)  #trim probed radius near concavities\n",
    "            pt1[nW] = tractCP[t]\n",
    "            pt2[nW] = Point(tractCP[t].x+CwR*math.cos(angle[nW]),  tractCP[t].y+CwR*math.sin(angle[nW]) )\n",
    "            pt3[nW] = Point(tractCP[t].x+CwR*math.cos(angle2[nW]), tractCP[t].y+CwR*math.sin(angle2[nW]) )\n",
    "            wedgePoly[nW] = Polygon([pt1[nW], pt2[nW], pt3[nW] ])  #.union(tractCPball) not needed; see below modifcn\n",
    "            for c in range(nCounties):\n",
    "                if wedgePoly[nW].intersects(countyGeom[c]):\n",
    "                    if wedgePoly[nW].contains(countyGeom[c]):\n",
    "                        wedgePop[nW] += countyPop[c]\n",
    "                        for tt in countyTractList[c]:  #note - a single wedge can't contain its home county\n",
    "                            wedgeTractList[nW].append(tt)\n",
    "                    else:  #partial county overlap (could be the home county)\n",
    "                        for tt in countyTractList[c]:\n",
    "                            if wedgePoly[nW].contains(tractCP[tt]) and tt != t : #exclude home tract\n",
    "                                wedgePop[nW] += tractPop[tt]\n",
    "                                wedgeTractList[nW].append(tt)\n",
    "            offsetPop[nW] = wedgePop[nW] - targetWedgePop[nW]\n",
    "            #print(\"t,w,loop,r,wP,tWP\",t,nW,counter,r3(wR),r3(wedgePop[nW]),r3(targetWedgePop[nW]) ) #DEBUG\n",
    "            dR = 0.5 * dR  #bisecting step size for next round ...\n",
    "        if counter >= nLoopPrint:\n",
    "            print(\"we didn't converge for tract,r,wedge,pop, tWP=\",t,nW,wR,wedgePop[nW],targetWedgePop[nW]) #debug\n",
    "            print(\"our start-stop angles (in deg) were\",r3(180./pi*angle[nW]),r3(180./pi*angle2[nW]) )\n",
    "        HDradius[t][nW] = wR  #WE HAVE CONVERGED FOR THIS WEDGE'S RADIUS.  SAVE FINAL RADIUS AND ANGLE\n",
    "        HDangle[t][nW] = angle[nW]\n",
    "        if counter > tractLoopCounter[t]:\n",
    "            tractLoopCounter[t] = counter  #we'll track the max number of bisection loops across all wedges\n",
    "\n",
    "    totalOffsetPop = np.sum(offsetPop)  #we have solved for all wedge radii to get each within one tractPop of target\n",
    "    if printDebug == \"yes\":\n",
    "        print(\"totalOffsetPop, components are\",r3(totalOffsetPop),r3L(offsetPop))\n",
    "    HDtractList[t] = [t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeTractList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "    while abs(totalOffsetPop) > avgTractPop:  #LOOP TO GET WITHIN ONE TRACT OF PERFECT DISTRICTPOP         \n",
    "        if totalOffsetPop > 0:  #we should remove at least one full tract\n",
    "            maxW = offsetPop.index(np.max(offsetPop))  #NOTE: we could later restrict this to non-map-boundary tracts *****\n",
    "            farthestTract = -777\n",
    "            maxDistance = 0.\n",
    "            for tt in wedgeTractList[maxW]:  #finding the list of HDboundary tracts in this wedge\n",
    "                if tractCP[tt].distance(tractCP[t]) > maxDistance: #... to select the farthest from center\n",
    "                    for neighborTract in neighborList[tt]:\n",
    "                        if neighborTract not in HDtractList[t] or isMapBoundaryTract[tt] == 1:\n",
    "                            #found tract not in HD that neighbors this tract, or this tract on map boundary                            \n",
    "                            maxDistance = tractCP[tt].distance(tractCP[t])\n",
    "                            farthestTract = tt\n",
    "            chgPop = tractPop[farthestTract]\n",
    "            chgTract = farthestTract\n",
    "            offsetPop[maxW] -= chgPop\n",
    "            wedgePop[maxW] -= chgPop\n",
    "            totalOffsetPop -= chgPop\n",
    "            wedgeTractList[maxW].remove(farthestTract)\n",
    "            HDtractList[t].remove(farthestTract)\n",
    "\n",
    "        else:  #we should ADD at least one full district to get close to aDP (totalOffsetPop is < 0)\n",
    "            #minW = offsetPop.index(np.min(offsetPop))  #this could pick a minWedge with no eligible addable tracts, so...          \n",
    "            qualifyingOffsetPop = [np.max(offsetPop)+0.001]*nWedges  #... we work around with this \"qualifying\" cheat\n",
    "            for nW in range(nWedges):                \n",
    "                addableTractList = [] #finding the list of tracts just outside this wedge in HD\n",
    "                if wedgeTractList[nW] != [] :\n",
    "                    for tt in wedgeTractList[nW]:\n",
    "                        for neighborTract in neighborList[tt]:\n",
    "                            if neighborTract not in HDtractList[t]:  #not previously considered ...\n",
    "                                if neighborTract not in addableTractList and (\n",
    "                                    tractPop[neighborTract] < avgTractPop - totalOffsetPop):  #... and not too big to yo-yo\n",
    "                                    addableTractList.append(neighborTract)\n",
    "                    if len(addableTractList) > 0 : #this wedge could add a tract, so it's eligible to be the minW\n",
    "                        qualifyingOffsetPop[nW] = offsetPop[nW]\n",
    "            minW = offsetPop.index(np.min(qualifyingOffsetPop))\n",
    "            closestAddableTract = -555\n",
    "            minDistance = 99999.\n",
    "            addableTractList = [] #finding the list of tracts just outside this wedge in HD\n",
    "            maxTractAddPop = avgTractPop + abs(totalOffsetPop)  #this prevents yo-yoing\n",
    "            for tt in wedgeTractList[minW]:\n",
    "                for neighborTract in neighborList[tt]:\n",
    "                    if neighborTract not in HDtractList[t] and tractPop[neighborTract] < maxTractAddPop:\n",
    "                        #found a tract not in HD but adj to this wedge, and not too populous to induce yoyoing\n",
    "                        if neighborTract not in addableTractList:  #... and not previously considered\n",
    "                            addableTractList.append(neighborTract)\n",
    "                            if tractCP[neighborTract].distance(tractCP[t]) < minDistance:\n",
    "                                minDistance = tractCP[neighborTract].distance(tractCP[t])\n",
    "                                closestAddableTract = neighborTract\n",
    "            chgPop = tractPop[closestAddableTract]\n",
    "            chgTract = closestAddableTract\n",
    "            offsetPop[minW] += chgPop\n",
    "            wedgePop[minW] += chgPop\n",
    "            totalOffsetPop += chgPop                                                                                     \n",
    "            wedgeTractList[minW].append(closestAddableTract)\n",
    "            HDtractList[t].append(closestAddableTract)\n",
    "        if printDebug == \"yes\":\n",
    "            print(\"> 1 tract off. chgTract,chgPop, totalOffsetPop, components are\",chgTract,chgPop,r3(totalOffsetPop),\n",
    "                  r3L(offsetPop))\n",
    "        # OK, we're now within an avg tract's pop of hitting the avgDistrictPop target\n",
    "    while abs(totalOffsetPop) > 0.001 : #loop to perfect the districtPop via a single tract partial.  Should only take one loop\n",
    "        HDtractList[t] = [t]\n",
    "        for nW in range(nWedges):\n",
    "            for tt in wedgeTractList[nW]:\n",
    "                HDtractList[t].append(tt)  #building the list of tracts in the Home District  \n",
    "        if totalOffsetPop > 0:  #we should remove a partial tract\n",
    "            maxW = offsetPop.index(np.max(offsetPop))\n",
    "            farthestTract = -888\n",
    "            maxDistance = 0.\n",
    "            for tt in wedgeTractList[maxW]:\n",
    "                if tractCP[tt].distance(tractCP[t]) > maxDistance and tractPop[tt] > totalOffsetPop:\n",
    "                    for neighborTract in neighborList[tt]:\n",
    "                        if neighborTract not in HDtractList[t] or isMapBoundaryTract[tt] == 1:\n",
    "                            #sheddable (boundary) tract; has a non-HD neighbor or is on map boundary\n",
    "                            maxDistance = tractCP[tt].distance(tractCP[t])\n",
    "                            farthestTract = tt\n",
    "            partialTractNo[t] = farthestTract\n",
    "            partialTractUse[t] = 1. - totalOffsetPop / tractPop[farthestTract]  #reducing this tract's usage           \n",
    "            if printDebug == \"yes\":\n",
    "                print(\"subtracting\",1.-partialTractUse[t],\"of tract\",farthestTract,\"(tractPop)\",\n",
    "                      tractPop[farthestTract],\"to lose\",r3(totalOffsetPop))\n",
    "            offsetPop[maxW] -= totalOffsetPop\n",
    "            wedgePop[maxW] -= totalOffsetPop\n",
    "            totalOffsetPop -= totalOffsetPop\n",
    "\n",
    "        else:  #we should ADD a partial tract\n",
    "            #minW = offsetPop.index(np.min(offsetPop))  #this could pick a minWedge with no eligible addable tracts, so...          \n",
    "            qualifyingOffsetPop = [np.max(offsetPop)+0.001]*nWedges  #... we work around with this \"qualifying\" cheat\n",
    "            for nW in range(nWedges):                \n",
    "                addableTractList = [] #finding the list of tracts just outside this wedge in HD ...\n",
    "                if wedgeTractList[nW] != [] : #won't work to add to an empty wedge list\n",
    "                    for tt in wedgeTractList[nW]:\n",
    "                        for neighborTract in neighborList[tt]:\n",
    "                            if neighborTract not in HDtractList[t]: # ... that were not previously considered ...\n",
    "                                if neighborTract not in addableTractList and (\n",
    "                                    tractPop[neighborTract] > abs(totalOffsetPop) ):  #... and can fill remaining gap\n",
    "                                    addableTractList.append(neighborTract)\n",
    "                    if len(addableTractList) > 0 : #this wedge could add a tract, so it's eligible to be the minW\n",
    "                        qualifyingOffsetPop[nW] = offsetPop[nW]\n",
    "            if printDebug == \"yes\" :\n",
    "                print(\"qualifyingOffsetPops are\",r3L(qualifyingOffsetPop))\n",
    "            minW = offsetPop.index(np.min(qualifyingOffsetPop))  #pick the eligible wedge that's the most under-target\n",
    "            closestAddableTract = -777\n",
    "            minDistance = 99999.\n",
    "            addableTractList = [] #finding the list of tracts just outside this wedge in HD\n",
    "            for tt in wedgeTractList[minW]:\n",
    "                for neighborTract in neighborList[tt]:\n",
    "                    if neighborTract not in HDtractList[t]: #found a tract not in HD but adj to this wedge ..\n",
    "                        if neighborTract not in addableTractList and (\n",
    "                                tractPop[neighborTract] > abs(totalOffsetPop) ):  #... plenty big and not previously considered\n",
    "                            addableTractList.append(neighborTract)\n",
    "                            if tractCP[neighborTract].distance(tractCP[t]) < minDistance :\n",
    "                                minDistance = tractCP[neighborTract].distance(tractCP[t])\n",
    "                                closestAddableTract = neighborTract\n",
    "            partialTractNo[t] = closestAddableTract\n",
    "            partialTractUse[t] = abs(totalOffsetPop) / tractPop[closestAddableTract]\n",
    "            wedgeTractList[nW].append(closestAddableTract)\n",
    "            HDtractList[t].append(closestAddableTract)\n",
    "            if printDebug == \"yes\":\n",
    "                print(\"adding\",partialTractUse[t],\"of tract\",closestAddableTract,\"(tractPop)\",\n",
    "                      tractPop[closestAddableTract],\"to gain\",r3(-1.*totalOffsetPop))\n",
    "            offsetPop[minW] -= totalOffsetPop  #remember, this \"offset\" is negative (we were underpopulated)\n",
    "            wedgePop[minW] -= totalOffsetPop\n",
    "            totalOffsetPop -= totalOffsetPop               \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST and perfect districtpop.  FINALIZE STATS\n",
    "    HDvPop[t] = np.sum(wedgePop)  #home tract's pop was divvied up among wedges\n",
    "    if abs(HDvPop[t] - avgDistrictPop) > 0.123456789 :\n",
    "        print(\"For some reason, did not converge on HD pop for tract, pop vs. avgPop\",t,HDvPop[t],avgDistrictPop)\n",
    "    HDtractList[t] = [t]  #finalizing this list of Home District tracts ...\n",
    "    #**** ADDING IN HD SUMMARY STATS HERE ********************************\n",
    "    totGOP = vtdTrump[t]   #start with the home tract, which is not in any wedge lists\n",
    "    totVote = vtdTrump[t]+vtdBiden[t]\n",
    "    totVAP = tractVAP[t]\n",
    "    totHisp = tractHisp[t]\n",
    "    totBlack = tractBlack[t]  #seed here, b/c we will add in by wedge\n",
    "    for nW in range(nWedges):\n",
    "        if wedgeTractList[nW] != [] :\n",
    "            for tt in wedgeTractList[nW]:\n",
    "                HDtractList[t].append(tt)  #... just in case we had the list wrong\n",
    "                fracUse = 1.\n",
    "                if partialTractNo[t] == tt:\n",
    "                    fracUse = partialTractUse[t]\n",
    "                totGOP +=   fracUse*vtdTrump[tt]\n",
    "                totVote +=  fracUse*(vtdTrump[tt]+vtdBiden[tt])\n",
    "                totVAP +=   fracUse*tractVAP[tt]\n",
    "                totHisp +=  fracUse*tractHisp[tt]\n",
    "                totBlack += fracUse*tractBlack[tt]\n",
    "                HDarea[t] +=   fracUse*tractArea[tt]\n",
    "    HDvGOP[t] = totGOP / totVote\n",
    "    HDvBlack[t] = totBlack / totVAP\n",
    "    HDvHisp[t] = totHisp / totVAP\n",
    "    HDtotVote[t] = totVote\n",
    "    if printDebug == \"yes\":\n",
    "        print(\"total HD pop, avgDistrictPop are\",HDvPop[t],avgDistrictPop)\n",
    "        for p in HDtractList[t]:\n",
    "            hi = \"hi\"\n",
    "            plotCenter(p,tractGeom[p])\n",
    "        plotPoly(tractGeom[partialTractNo[t]])\n",
    "        for nW in range(4):\n",
    "            plotPoly(wedgePoly[nW])\n",
    "            plotCenter(nW, wedgePoly[nW])\n",
    "        plotPoly(countyGeom[countyNo[t]])\n",
    "        plotPoly(finalMAP)\n",
    "        plt.show()\n",
    "\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(isNonEquiAngle)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dd30e1f0-be6d-4906-ba56-5cb9c17f6400",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDtotVote = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    for tt in HDtractList[t]:\n",
    "        HDtotVote += vtdTrump[tt] + vtdBiden[tt]\n",
    "    HDtotVote -= (1. - partialTractUse[t]) * (vtdTrump[partialTractNo[t]] + vtdBiden[partialTractNo[t]] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "65e442fb-b9f0-43e8-9d5a-7465c005d07c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now capture the unpatched HDpiece results in a csv file\n"
     ]
    }
   ],
   "source": [
    "#start 9:02, end 9:46\n",
    "print(\"I will now capture the unpatched HDpiece results in a csv file\")\n",
    "date = \"14Sep\"\n",
    "#write another file - tract Use and GOP lean - don't write the geometries\n",
    "tractNo = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"tractPop\":tractPop,\"HDwt\":HDweight,\n",
    "                    \"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDtotVote\":HDtotVote,\"HDvBlack\":HDvBlack,\"HDvHisp\":HDvHisp, \"HDarea\":HDarea,\n",
    "                    \"angle0\":angle0,\"HDtractList\":HDtractList,\"partialNo\":partialTractNo,\"partialUse\":partialTractUse,\n",
    "                   \"vtdBiden\":vtdBiden,\"vtdTrump\":vtdTrump,\"neighborList\":neighborList} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HDpcL\"+str(levelL)+\"lLmAR\"+str(maxAngleRatio)+date+\"unpatched.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f34423b0-b0d1-475b-8ff3-f6d31e599226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now compute the tract usage\n",
      "and plot the tract usage distro\n",
      "1.0 0.21141 = avg,std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now compute the tract usage\")\n",
    "tractUse = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    for tt in HDtractList[t]:\n",
    "        tractUse[tt] += tractPop[t] / avgDistrictPop\n",
    "        if partialTractNo[t] == tt:\n",
    "            tractUse[tt] -= tractPop[t] / avgDistrictPop * (1. - partialTractUse[t])\n",
    "print(\"and plot the tract usage distro\")\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * HDweight[t]\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 * HDweight[t]\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "print(r3(avgTractUse),r5(sdTractUse),\"= avg,std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=HDweight)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ef1b7278-b0de-4bbc-b91f-111e82c9fe5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.0019 0.43251 HD avg vs true 0.43061\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9db8f486-ca70-4150-8636-ffbdd4a9d07a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the correlation of HD area to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here is the correlation of tract usage to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABn4ElEQVR4nO29e5gc1Xng/XurZ0ZIYiSNdb+NhLgIrME4kkCSjbkY7DVeMDaXYHCc9SZYOGt/iXP77HVimVUSr/Mk+RZnQxYwy3q9iwQGiWtMgmWLm42ENGNAM4CQENJoNLprJA1IaKa7zvdH1ak+VV3dXT3TPd0zOr/nEUxX1+V0dfV5z3sXpRQWi8VisURxqj0Ai8VisdQmVkBYLBaLJRYrICwWi8USixUQFovFYonFCgiLxWKxxFJX7QGUk0mTJqm5c+dWexgWi8UybGhtbT2klJoc996IEhBz585l8+bN1R6GxWKxDBtEZFe+96yJyWKxWCyxWAFhsVgsllisgLBYLBZLLFZAWCwWiyUWKyAsFovFEosVEBaLxWKJxQoIi2WIad3Vw93rt9O6q6faQ7FYCjKi8iAsllqndVcPX7p/A31pl4Y6hwdvX8qiOU3VHpbFEovVICyWIWTDjsP0pV1cBf1plw07Dld7SBZLXqyAsFiGkKXzJtJQ55ASqK9zWDpvYrWHZLHkxZqYLJYK0rqrhw07DrN03kQWzWli0ZwmHrx9aWibxVKrWAFhsVSIfP4G/W+4ExV+lpGHFRAWS4WI8zeMlInUOttPD6wPwmKpEOX0N9RaaKx1tp8eWA3CYqkQxfwNSU00tbha18KvP+1aZ/sIxgoIi6WC5PM3tO7q4db7XqY/o6hPCauXL8s76Zur9b60y8qnOmiZOZ4bFs6qmqCwzvbTAysgLJYqsKati76MAqAvo1jT1pV3ktWrdS0kXus6xmtdx3iktYvVX62eNjFSnO2W/FgfhMVSBQ71ngq9fuXdI6za2BnrZ9Cr9Y+fMym03dr+LZXGahAWSwFKDeVMsn/rrh72H/8gtG37gff4zmNbcIRYP8OiOU188+rz2LjjcKB5WNu/pdJYAWGx5KFU57C5vyPCyutbuG1Jc+w+p/rd2HMUColdNKeJ1cuXsaatC4Gq+iAspwdWQFgseSg1j8Hc31WKFU+0M39aY/De0nkTg30Unn33wlnjeXPvcdIZhQs4RUJio3b/fBqLTWKzlAMrICwWn+ikunTeROpSXihnKlXcnNM0piH02nUVa9u6eGTz7iBa6c7PtYTCQ1dctwDwBEjTmAZ6TvSVZM6K03BqMSzWMjyxAsJiIX6yBUCp8P8LHL/y6Q5cfzcBGuodDvSeCkUrtXcfiw0PLTaBx2kE+TSckZzBbRlaKhbFJCKzRWS9iLwpIh0i8kcx+4iI/KOIbBeR10VkofHeZ0Rkq//etys1TosF4ifbDTsOk3YVCsi4qmDEkD4evB/VpedO4sHblyKR/QRPGHz9ynMST9paeP3Ds1v50v0bgiinfJnatmKspVxUUoNIA3+qlGoTkUagVUR+rpR6w9jnGuBc/98S4H8AS0QkBdwNfAroAjaJyJORYy2WspEvMzhptnD0+G9efR4Az719MNinLiXcsHBWyWPLV9Ziw47DrLh2QaxZ6saFs1D+/4dKe7B+j5FHxQSEUmovsNf/u1dE3gRmAuYkfz3wE6WUAjaIyAQRmQ7MBbYrpXYAiMhD/r5WQFgqQr7M4KTZwnHHf+exLfT7WoUAtyyendiRbL4XFT5NYxry+hiiprIbCwikck7o1u8xMhkSH4SIzAV+C9gYeWsmsNt43eVvi9u+JM+5lwPLAZqbm+N2sVgSEZcZbNr7zdfFjm/d1cOjrV1oz0V9RHsoNKHGvWcKn0I+hqT+h3JP6NbvMTKpuIAQkTOBNcA3lVLHo2/HHKIKbM/dqNR9wH0AixcvLuxJtFhKZKAT6YYdh0lnstrDzRHtodRJPuqzyGf6SlpEr9wTui3eNzKpqIAQkXo84fCgUmptzC5dwGzj9SygG2jIs91iKUo5TScDnUijE2bU91BoQi022RYqlJe0iF65J/ShKt5n/RxDi6gi4XsDPrGIAP8bOKKU+maeff498A3gs3gmpH9USl0iInXA28BVwB5gE3CbUqqj0DUXL16sNm/eXL4PYRl2lNt0os+ncyFuWjQrsOsnKalRaJ+kPohSxl/KccNtsrV+jsogIq1KqcVx71VSg/g48GVgi4i86m/7DtAMoJS6B/gZnnDYDpwA/qP/XlpEvgH8G5ACHigmHCwWKL/pRK+MdcLbQ6908ujm3SBCOlN4oipW7bTQ+wOplFrKBFpp4VCJ81s/x9BTySiml4j3JZj7KODred77GZ4AsVgSUwlbuE4+S7vKm5wyCvDyI2pposo3gUYn60qsxM1rbN3Xy4on2sm4Xvb4zYtnl6VulPVzDD02k9oyoqiULdycnFKOgAiZTG1NVHETaJwwKPdK3LxGnSNklJdYCF72+KqNnaxp6xq0ILJNioYeKyAsI45KNLKJTk5Q3AcxWEox06za2Mkz7Xv5yrK5NI6uD465e/32HGFQ7pX4hh2HOdXvFSDUZUVMyqlp2SZFQ4sVEJYRS7nt4NHJabCr7mIO7KRmoFUbO/nOY1sAeHHbIb7/hQuDfeOEQakr8WJjbRrTEIpBr0sJrqsQwHG8v2tJ07IkxwoIy4ikliNekoytFDPQM+17c17rPhT5hEHSlXiSsfac6MMRr5eFI17G+IwJo4dM07JUDisgLCOSWo54STK2YmYgc1V/Tct0Xtx2KHjvmpbpoX2jGd6lTNhJx6rLotelnByHdK3cd0vpWAFhGZHUcsRL05gGHBFQ+U0vhcxAcav673/hQp5p38s1LdNzutgVOq7Y5J1EUK1p68JNWBbdMrywAsIyIqnViJds3wiF4wgrrl1Qci5EvlIc+QRDoeOK3Zd891ELhkdbu+j3O+RBtiz6QO73YBILLZXBCgjLiKWcES/lmpzMSVpQ9JzoK/kcA9WOBnpc9D6afbVNfUEYeP+JYtpNLfuURjJWQFgsRSjn5FQO09dAtaNyaVVmX23wBcMgE+KKaTe17FMayVgBYbEUoZyTU7km6VKikMxrlUOriiYNliNTOp/g1ONvGtNQsz6lkYwVEBZLESpR+XQoVr9JzDYDEVSV8O/EnTM6/nzd8yyVwwoIi6UIxSbEWnWeFmpV2jSmgZVPdwzYbFapbPVCZqWeE318/cpzynpNS2GsgLBYEpBvQqxl52mhVqWOCBm39goOmtRyqPLpghUQFssgSOKfGGoNw7xevlaloEg5giqQi1GpMQ21U90ycKyAsFgGQZJEsqHUMOKuZ5plAudyyuHy8yYzpXFUWUpxlzqmUoSEFQzVw6n2ACyW4Yxe5f7Jp+cnrqlUSQpdT4/1i5c0g1L84s39rGnrquh4io3JUttYDcJiGSRxiWTaLDLUdvQk/axDzY+GwP9gfQnDFysgLJYyEmdOGUo7ehK7/VBP2NaXMHyxAsJy2lNOJ3K+OklDOSkWs9snmbAr3UvDMjywAsJyWlNuJ/JwMacUmrBrOXTXMrRYAWE5rSl3jZ+RYE6xdY8smooJCBF5ALgWOKCUaol5/8+BLxnjuACYrJQ6IiI7gV4gA6SVUosrNU7L6U0lVvzDyZwSZ0oaLlqQpfKIqlCDDxG5DHgP+EmcgIjsex3wx0qpT/qvdwKLlVKHCh0XZfHixWrz5s0DHLHldKVWS2VUmkKmpNP1npyOiEhrvkV4xfIglFIvAEcS7n4rsLpSY7Gc3rTu6uHu9dtp3dUT+/6iOU2BI7nYvpUey1BSLGdCJ9jVyngtQ0/VfRAiMgb4DPANY7MCnhURBdyrlLqvwPHLgeUAzc2FO2pZhp5yrUQHep5SHK6Vds7WmvO31rLALbVH1QUEcB3wK6WUqW18XCnVLSJTgJ+LyFu+RpKDLzzuA8/EVPnhWpJSrglmMOcpxeFaaedsLTl/tcAtVEK7lsZrqQ61ICC+SMS8pJTq9v9/QEQeAy4BYgWEpXYp1wQzmPOU4nCttHO2Vpy/psCtSznctGhW7H61MF7rC6kuVRUQIjIeuBz4HWPbWMBRSvX6f38aWFmlIVoGQbkmmMGcp5Sw00qHqNZKCKwpcPvSLqs3dvLo5t05neGqPV5r4qo+lYxiWg1cAUwC9gPfA+oBlFL3+Pt8BfiMUuqLxnHzgMf8l3XAKqXU3yS5po1iqj2q7YOw5KIn3lP92b7S4PWWHlVfOxPx3eu38w/PbsVVkBL4k0/Ptw2DKkChKKaKaRBKqVsT7PNj4MeRbTuAiyozKstQU66cgErmFpxuwkdrBmvbunhk8276M17jIAWc6ndZ29ZVE/ehFkxcg8XsqT0c26XWgg/CYqka5TZjDBdhowVu46g67n1hR7BdAY9s3l3xHhFJqLaJa7BENTVHoM6RHFNeLWMFhOW0ppyROsPNZt66q4f7X3qXqJE5nVE1E7E0nLLSo+hnS99fV0FfRrFqYydr2rpq/vkA2zDIcpqjzRgpYdBmjCSNcWotUS7j5vogHUfKYs4Zqs9aS/fUZOm8idQ5krPd7ANe61gNwnJaU04zxnBLPFs6byKj6h0+6HeDbXWOsPL6lkGPa6g+a63dU5NFc5q4efFsVm3sROEFATiOwBD1AS8HVkBYTnvK6UgvJGwKmbOq4btYNKeJFdcuYMUT7WRcRcoXDrctGXxFgqFKsqv1ZL4bFs5iTVtXsGgolJhYi1gBYakJhotztxiFhE0+DaOaq+CeE324yo9iUoqeE32Jjiv2fZUagTTQ79+8TsoRuo+epHVXT808Q1oIP9O+l2tappdF+A4lVkBYqk4tmwnKST4No1Kr4CST7kBCSZN8X6WY7gbz/evrrGnr4tHWLla/UlsO4NZdPax8uoO+tMumnUeYP62xJsaVFCsgLFWn1s0E5SROw6hEvH/SSXcgPpik31dS091gv/9Fc5rYsOMw6UztPUMbdhwOwlz7+mtnXEmxAsJSdUZCQtRgqES8fymTbqk+mHJ/X+U4Xy0+Q627enht99FsmCvQNKahmkMqmYqV2qgGttTG8GWk+CCSMBSfVWsQSZ2jpY6p3J8hyfmK7VNLz1BcORNH4NZLmpkxYXRNjFFTqNSGFRCWYUktTQZJMEsuaJt0pf0tSa85HHxAw2GMJmYdKfBCXOvrHFCKtKtq6jNUpRaTxVIphttkYY7XESHjqlCyVKXGrk1Hd6/fXtDcNBx8QMNhjCZL502kLuXQl/ZyTESguWk0Ow69P2w+A9hMasswJEnGci1hjtdVXr5BSiCVctjjh2UWYrCZwsWyxcuZTV4phsMYTRbNaeKmRbPQedSugu0HPeHgDJPPAFaDsAxDatEhWYjoeFdcu4CO7mM8snk3D73SydoCYZnl0JaKOcGHqijeYMyCw7Fw340LZ7G2rSvshwA+fs4kvnn1eTlmvlr8bFZAWIYdw22yiBvv3eu3k3ZVUXPDYEwr0Umn0HGVLopXLkFX69+1if7e733+HX7x5n6UgoZ6J1Y41KrJ1AoIy7Ck2pNFqSu+6HiTakED1ZZqbdIZbj6EcvLc2wfJKEg5woprF5RUgqXaWAFhsRgkDbestNmn1P2i1NqkM1RmwVoz1axt6woc1RlX0dF9DAiPs5ZNplZAWCw+SSf+UiffuEmrlIlsINpSrU06Q2EWHEqtKen3F00iUHnGWasmUysgLBafpBN/KZNv3GQAVHwiq0U/jVl7ynxdLoZKaypFEN24cBaP+m1d61PCjQtnxY7z61eeUxPfURQrICwWn6QTfymTb76Q3KGYyJJoHuXKoB4q01whhkpryieIzHug92sa08AV86ew//gH3HJxc/B5a0m7K4QVEBaLT3TiBy8jNl9oaJLJLd+kVQsTRKkTdr79K2WaK5Wh0privlPzHogIIpDJqJCJ6c297UE116TPWbWpmIAQkQeAa4EDSqmWmPevAJ4A3vU3rVVKrfTf+wzwQyAF3K+U+kGlxmkZeQw23r6USS/J+eImrVow/5Q6YUf3X9vWxYYdh+k+erLspjkY2Pc4FNFt+cKW9T0gT/mifqPXd7mfs0pRSQ3ix8A/AT8psM+LSqlrzQ0ikgLuBj4FdAGbRORJpdQblRqoZfhRyNRRjh9cOVe7cZNWvolsKKNw8q2E810/1Jwn5fDI5t2kXUWdI9SlHDKZgZnmotds3dXD2rau4Py1OHHGhS07IrgFats5Qs69qbVosygVExBKqRdEZO4ADr0E2K6U2gEgIg8B1wNWQFiAwkKgXD+4aoRlQuWd1yZxpo5C1zf333P0JA+90omrvPDNWy6ZzcwEVUqjE2v0u1xx7QJWPt0Ryj6uxYkzyqI5Tay8voUVT7Tjuoq6lHDF/Cn8cusBMhmFI/BXn78w57PvOXoykXCtFtX2QSwTkdeAbuDPlFIdwExgt7FPF7Ak3wlEZDmwHKC5eXi187MMjEJCoFwTezXCMm9YOGvIV5PmhJ2vqF9cRrZe5ev7fOPCWWXR1J5p30tfOiscdBXUWps447htSTPzpzXmaEPFNN06R/jiJc3cMMB7WEmqKSDagDlKqfdE5LPA48C5ENS3Msmrtyml7gPuA6/cdwXGaakxCgmBUib2YuacStuzo5OjUF3ndTHnq6lVmPe5aUzDgENXo9e8pmU6m3YeCcxYNy2aNWDhUw1MAaodz1+/8pyc/czvPuMqZkwYXZOfsWoCQil13Pj7ZyLyzyIyCU9jmG3sOgtPw7BYgGTF56BwvH0tOAejk+MNC2dxgx8nX42iecWcr2aYrpkFPJj7GHfN6Cp8uJHk2aq1RMZ8FBUQIjIG+FOgWSn1VRE5F5ivlHp6MBcWkWnAfqWUEpFL8AodHgaOAueKyFnAHuCLwG2DuZZl5FFodb9qYycrnmgn4ypG1cf/QKvlHIxOznGCbrDjKGTWKOb8jXO+mhNZ05iG0OR3o2EW60u73LXu7ZxidMWIXnMoIpEqReuuHu5a93ZOtNeati4O9Z5icuOowJRUC5FsxUiiQfwvoBVY5r/uAh4BCgoIEVkNXAFMEpEu4HtAPYBS6h7gJuAPRCQNnAS+qLz2dmkR+Qbwb3hhrg/4vgnLaUzSchWtu3pY8UQ7ab+V16n++EmrGiu4QuaaSl8jrgVmEsEYnciiglUBdSnvProKXtp2iE07j9Rc1NFQEL3Hjng9Px7e1IlfjgmAhzfv5uHly4aFIEwiIM5WSt0iIrcCKKVOikicnyCEUurWIu//E14YbNx7PwN+lmBsltOAUspVbNhxOBRqqIBfbc+dtKqxghsKrSXfNfT2gTh/oxOZKVhbZozn0c27g/MORae8alLIb2XeY933YfaHxrBqY2dov3RGsaata1jcnyQCok9ERuM7ikXkbOBURUdlOW1IEvdfSrkKrRnoCppKUTMx5pXUWsz+03HXiOYwDNT5G6dRaG1Nk0oN7LPVWiXWKNHWsSuvb+G2JdnIyej3+82rz+PnHftiz1V0hV0jJBEQ3wP+FZgtIg8CHwe+UslBWU4P4mLge0705UwQpZSriEbXrHy6I3ZCroaTulJaS6H7CNkyDuW4dtwkHk0Qu2lRvOApJACqGTSQVDCZCxVXKVY8kS2dAfHf78qncq3jKUe4YeGsin2eclJUQCilfi4ibcBSPMH3R0qpQxUfmWXEY/7g+tKul2Skcp2n+SbWfGGWpkkkX0RMtZzUlbA7R+9jz4k+vn7lOaza2Ml3H99CRkFDSli9fFlsyGVS8k3iZoJYQ71ndorWFiomADbsOBzY7vv6hzZoIKlgimZLu67KGWf0+x1V54TOIcBfXd9SsgCtFkmimD4OvKqU+hcR+R3gOyLyQ6XUrsoPzzKSMTUDESHjqrw27LiJVb8ulv0LuSGvpZaZGEpKHUfTmAa0lcdV3uvWXT2BcADoG6TdOy46R39HZoJY05gG7nwqq7Wt/mrWN6SPPdXv8qc/fZXll50dmGiaxjQEfgzXf10pzPtbykIhThgWK/Xe1tkT2nb9R2eEzFLmvtUOu44jiYnpfwAXichFwJ8DD+DVV7q8kgOzjHySmoMKUewHXizRa01bFwJs3dfLyqc7qv4DHchE0XOiD4HAOdpzos931of3G6jdOy46x/yOzAnX7KDWl3a59/l3uGj2hMA3os+x8/AJvvPYFsDLQO450YcjnoBzxPsMlSDOHFeKXyguWzp6/rB/Jnz8k6918+Vlc2sm7LoYSQRE2s9VuB74R6XU/xSR/1DpgVlGJnFlG4qZg+KO0xRz/Bb74ekJTZsOqv0DHchEsXTeREbV596D+pTQ56sQ9amB273jonO+efV5AHznsS082tpFOuNNuBfOHB869tk39rPuzf3BZHzfC++w8/CJ4P1n2vdy25JmmsY04Ign5hqGsJdDz4m+kn0z+cyE0fIZV8yfEghujau8Z06HHpsJh7WYOJdEQPSKyH8Gfge4zK+2Wl/ZYVlGIsVWx0l+eEn9E5pCPzxzskApHEcQVFV/oAOZKPLdg9XLlwUa0mDq/MRF5wCxeRU9J/pzjjd9I8svOzvQHACuaZlO664eVj7dQcZVpBzhK8vmDrh0R6mfxVyoDJaQLyij+Pkb+2P3O9h7ati0HU0iIG7By2T+faXUPhFpBv6ussOyjEQGqkYXO66QYNmw43Di6Kh8+w0l5Yh0Mlem3//ChYM63jTHmVVftT/CzKtIpRzePfRe7DkdkdDneaZ9L9e0TOe2Jc1BOQ+FV5foRy/uQEFFTH2F7u9gfVD6edJCUxFv1pvUOCr2ma7FtqNJopj2Af+f8bqTwj0eLJZYBqpGL503kTpH6M94K8wkxyWx5Q9FstxQOL5Dpo2UA0oNuI9CIZ+NNotE/RF1jnDz4tkoYHUkKQy891cakTu3LWnOyR+oSzlZIeFLncGY+lZt7AwJIZO4BUU5nMSmX+vR1i4yGS/nJJ1xQ/6g7ft7Odh7KqfMd60ESZjkFRAi0kvYfKaAQ8B64FtKqcMVHptlhBE3ISf+Ufj2aYon8QPJtZVKhJ1qBjLpDOSY6GeF+IzmgSYlxoUHm/6Ia1qm03Oij6YxDdQbSYp1KeG3F88OJeTl69t8wbRGXus6FlynlEzvKKs2dgZmrBe3eRH5cZFDST93KRO3fp5uNIourmnrCmVTv7LTi2xKCbTMHM8tF3tjG1ZRTEqpxug2EWnCS5K7B7i5csOyjFTMCbmUXsbpjL+6zCR33Fbb6TcQk9pAndRmljRKkXFVTqRRkntd7L5F3584toG/fHwLSsGoeoc7r1tAR/cxFORkasdpOv1+32Yt9h2grm5wZb6fad+b8zoqIFp39YT8M/nCnrU2oJ3wSSfu6MLj0c27g4ABTUbBa13HaN+zhbMmnxloZcMtiilAKdUD/DcR+XKFxmM5jUg6GQ7Ucbvi2gWBmaEaP7aBjLscTmogZ8W7pq0r0QSUxOx248JZKGDcqDrueWFHsP1Uv+eI/ps8fo98mo7+vyPZCKlSzWLmeKM65uj6FK27ekILk1vvezmYsB9p7WL1V5fm3MOBFDfMi9aAY8go2H7A89041FaDpJL7QYhI/UCOs1iiJJ0MB+Ir0JExfWmXTTuPhEoiDBUDGfdA/SLRFWt05f5oa1cwPRXz4ySNJps/NWxkEMntuWwSp+mkMwoXTzg0+BFSpQqHW3+0IXiGPtsyjRe2ZQs9CLDuzf28sO1gqKBjv7Gaj3MSm45zfZ6BTtxaA07Cx88tXUBWkkI+iBtiNjfhRTU9WrERWU4bSpkMS/UV1EriUbFxx9m3B+oXyWcrNycoAW5ePHtA54/e06njzgCyfoPPXTQjNiqoaUxDEB2mtboF08fReyqNAlpmjB9w9Ni9z78TSsxb92Y4tNSPYKYvHS7oGMoRKWJKS/lO+IGGCpuZ7oVIOVJTwgEKawLXRV4rvIY+P1RK/UvlhmQ5naiUk7gWfBDFSFqscLDnit6LgSbMRc9zx+VnM2/SWO57cQeugqdf38slZ03ktiXNwXg+6M8KpvqUgAjpjMuL2w4heH6LJL6GfL0/fvHWgdB+Z9SneO9UJud4HWYL3jNXLEeknBFuSbLCRfLXaKomhZzU/xG8WkxKqV+Z78Vts1hqiaEIYR0socSq/vzFCks+Vzr3XOW6FzcsnBWaVDfsOIwu5Jp2sxVOdfE9jcJLHhNUyb0j8jnYvWuHl+aH3sudjAVCYbaQP9Q1X5b/QIiWYNeaTsqBRc1NtHYexfWTA6Olw2uFJL6E/w4sTLDNYqkpKhnCmo9SQiKjxQoHU+qjWOHDwSZhRSdprYUsnTeRlCNBTwhXqeDzm9vBC+s08wKSOmTzmQujiWn5+NSHpxadfMtZLC8u+umzLdN44tVub5wKWjuPknFVkCNSi8IBCvsglgEfAyaLyJ8Yb43DawVqsVgMSp1kTC1noMUKK3Eu8/MUq3q6aI5R4VRl6ygtmtPE7ZeeFYpyWjiniS/81qwgZyKpOS2fuVBHqpktZjWOeL6H+pRwx+VnF/2spfqsCvX9jkY/9aVdnnytO3id0Y4RvJLhlSpMWA4KaRANwJn+Pma4wnG8ftIWy4gg6aq/2H7RSWZtW1fR85paTqFihUko57lKqXqar8Jp4+j6ULG6TTt7+E3n0ZJXzIXMhT0n+kLNigDqHFh5/YUl+XNK8VkVWgiYiYTgmbccX6OLw0lYGaBaFPJBPA88LyI/tr0fLCOVpKv+JPtFwzgf2by7pJIX5TSJDfZcpVY9jbuerjJrrqZNP0Upwjjf51k6b2JOR7tPnp9rUiom3JP4rPQ59hw9mVfb0GVhdISUCJw75Uy27u/FlGMCge/BdLrXms8siQ/ifhG5WSl1FIJs6oeUUv+uoiOzWIaApKaFJPuZk8yeoyd56JXOqofZJiU6OZWj6qm+H2vbunjolc6gxlImphObOQ5tv9chpnEah+kAvvL8Kfzizf1BKOlzWw+EEuNWbexkxRPtZFzFqPr8wjr6+aJlQcws8DpHcrLV9TluXjybVRs7UXiVbN/c15tzrY/MGs+K6xaEhMOwKrVhMEkLB/CyqUVkSrGDROQB4FrggFKqJeb9LwHf8l++B/yBUuo1/72dQC+QwetHsTjBOC2WkklqWiglqW/RHK/G1Nq2rpoOs9Xkm5yS1M1KsjJfNKeJRiPrWhHfMS7Ofh+nccQVDDSzp00B1LqrJ+SjSNrONM4prxcImYzLFy9pZsaE0bGf+4aFs0KZ63FMHXdGqKR5reTtREkiIFwRafaruCIic8iXMx7mx8A/kb/y67vA5b7AuQa4D1hivH+l7X1tScpA1fOk4bClhs1WMsy23KaIQg7oQitcSF5grnF0fdGOcVH7vUZHRul9un0Tjwre17Z+QBGqpXTXurdD9v+kNv/oPRHIySUpFCYbpzmBLosuPLf1AOve3B9Uwl0wY3xN5u0kERB/AbwkIs/7ry8Dlhc7SCn1gojMLfD+r42XG4CBZe9YTnsGq54nNZ0MxMRS7lVgJUwRSbSjOCECJF716o5xSikckVgNIhyqCwpv/4Y6h6YxDaFubXUph3TaxYWsI1x5dv0V1y5g677ewKykiwFGbf6l3JMbFs5iwYzxeWt7xX0vNyycxSOtXWTSLo7AYv+Y7Qff48j7XmOlvoxi1cZORtXnT5Sspm8iST+IfxWRhcBSvPv8xxVY2f8+8Ix5WeBZEVHAvUqp+/IdKCLL8QVWc3NtxhJbBk+hH0mtqueVIElZ6mIhpMUaAuVzBscJkSSrXrNjnMIzG618uiPHUR0dBxBkO7d3H8uaeFzFLZfMZuaE0TSNaeCZ9r38avshXAVKKTq6j/Hwpt2BWUmAS8/NliVv3dUT3MukTmugYG2vfAJUlzhxVbbMdxSFJ2ifad+bU2qj2r6JpEX3MsAB4Azgw+KtBF4oxwBE5Eo8AXGpsfnjSqlu39fxcxF5K9/1fOFxH8DixYuTmL4sw4xiP5LhUFajXEQ/a9OYBu5evz3IfdCTlC5joe+VKTz0fnENgfKRT4gUEizRqB/zx5nPFxA1a+me4VHHsFmeY/60Rja+eySIHlMQMiuJwILp44LPnbSpkjkWXbwv3yIk7hncuq83J8IqSsrxtCRXwUvbDrFp55HYsNlqLX6KCggRuR34IzwT0Kt4msTLwCcHe3ER+QhwP3CN2YBIKdXt//+AiDwGXAKURSBZhh/FfiSVtPfXGuZnNSd7x8jEhmwZi7VtXaxt6wpCbp2YLOsk5ox82/MJlmjvB+1/0LiEHdVx5ze/90KO4a37esn4/UJQipYZ40PhtUrB/S+9G8pUN+9Rkkm32CJEfy9a49m6r5eVT3fgKq+UBkoRaQdBSrz6S8+07+WlbYdix1PtxU8SDeKPgIuBDUqpK0XkfOC/DPbCfm/rtcCXlVJvG9vHAo5Sqtf/+9PAysFez1JZKmknTfIjqYS9fyAMhb1Yf1ZzVauUQiRrj3fwylo8vKmTdKjStApWrdGGQqYgMVfW+RzUpvml0OSeTruI9iAbtHcfC64dpyFqvwWoUJHBtW1drGnr4kb/9Yon2rNtSjOKZ9r3BlVjtenJdRWOIwgqp6mS1sJKDVKI+661xuNItsyIKMXUcaPYd/xU6JyOI8yf5uUgv/zOYdw8YbPVXPwkERAfKKU+EBFEZJRS6i0RmV/sIBFZDVwBTBKRLuB7QD2AUuoeYAUwEfhn8dpI6nDWqcBj/rY6YJVS6l9L/2iWocIMO6xE4bFq/0iSMtAWo3E29wUJSmBrwZkVEl7/54+dPZHOIycYXZ/KicGPqxobrbwKXvOftW1dsSGYeoLWDmNdodX8zHG1oaIc6vUmzHz2e+23MB3P3/Udz+B1abt58eyQCUcBv9rumWpWXLuATTuPBAsL83Pr60ZNbiuuzd8Rr1hUl/k5VGRMUeEAkM4o1vganqs8Abbi2gUAIYFVzcVPEgHRJSITgMfx/AE9QHexg5RStxZ5/3bg9pjtO4CLEozLUiPoyp3aAZkkU7ZUBvMjGYpVvQ6pLNa1LW/ylSO4eJOGyRl5Erv0eb6ybC7/2rGPXYdPBLZ3s2GOyUWR5CyNnthMFPDI5t2x7Ti1U9VV+I13VMikZYZ66kk4rl6SfhWnIZohr6bj2RQ0fRnFgd5T1KWc0Ph1RVutSeSr/RTVwvr63RwBtHr5sthnJk6omZ8DyDEpRalLCWLcS0GxfuuBYAwNdQ6rv1rdhLkkUUxf8P+8U0TWA+MBu6K3BOSr6FmJB7vUyX4ookDMa2jzTpwprFDyle7NHMV05sY5ms051/GzxUIlHYzcgDjhAPHlKiCbcPb1K8/JiejRSYApR7yFQcZbAUdNVF+/8hwAOg+/HyrcBzClcRSQX0OMCqU4LeSXbx1gYfMENu/sCd0/0+m74toFWQd1pPlPVNMxhVh/Jvc5jpbwjmaa68/x/NYDeaOWNApoHFUXmNFSjoQywvvSWS2uWhQUECLiAK/rTGi/PpPFEmLRnPiKnuVmIJP9UESBmNco1Fc5OpZDvaeCyaHOF7DROdBxvJyBv3hsS6yjOdgP77oTxzbw+KtZBf+OT8yjcXR9Ufv6yutb+O7jW4JVryNhIRfV4Ezt4M4n2wHPzu+qeOdv4+j68OcSQo2LouePOuM7uo9RnxLSviDVnz3jKjbv6iHlgOt6zm+NHscz7XuzWoKfd7CmrSsnY7xpTAN3PtURaCP1qXBSXZIGT/pz7Dl6sqiASGcU9724A6W877l54tigN7X5GapJQQGhlHJF5DUzk9piiSNfRc+BUKh1ZqmT/VBEgUSL9M3+0JjgvVUbO4PkqqXzJlKX8vZzHC+bVtvY7/ycV43mLx/fEgiJlMDtl57Fyqc7ImUbvGN0VI7gmSuuaZkeTNYC3HHZPL792QsSfYb50xo9R3JGkXLgloubAxt8obaod6/fTtrNTtpxDvDgHhltPh2t7pBrdjP/3nP0JD/8xTbSGS8a6qoPT2H91gMhU5xSnjmnaUw9PSf6c/pIX9MynU07j2Sjmsj6WMzrLZrTxPxpjaxt64r1QcQVMPz6lefQuqsn8Bno/VpmjKchJXk1Q43+rjOuyhEOdQ6BI75aJPFBTAc6ROQV4H29USn1uYqNyjIsKcVPUKyefpyWMJDJfigc3PoaOgrooVc6WdvWxVeWzQ3MKi9uO8TXLpuHq1RgktETh1IqmGyiE1Rc+Qm9em3vPuZpFRkFIqzfeiCYgBVw/FQ6NM5CiXRr2rqCSdfP7cobwZQv/j/lCFfMn8KkxlG0zBgfqjW0aI5XxO7Bjd46081ky2eYfhjt8K5LOWQybsiO3592OXD8A9wYU5MCjpzwspMdIceUNH9aI2v870dP2g9v3h0btVUsKMB8/swADUdAHMH1GwFdMX9KkMXdc6KPTUU0CvA0wQtnjWfBzPGJWrFWmiQC4ky8onsaAf62MsOxnA4kqacfpyUMdLIfiigQHe2jJ58P+l1WvRJWuh9/dU8wCZtTXMqoD7R1Xy+dR06EyjmY5aN1RM9tS5q5e/32wNSUybi8e+h983JBlBDEF7gz771AzrF3r9+eU9o62uPCjP9/ZPNunn1jP45449Qd0/REvWDG+OD8LvD81gPh82dU0JA06jTX96yj+5hXZsOXYkrlmmHiTHzmM7Dar7RqaiFJgwqiLVfvXr89uKeZ4D+eKevZN/YDWU0mN9A3S8r/Agr5iqpBEgFRF/U9iMjoCo3HchpQSAgkSUgq5cdTyKld7uimpjENoQng+AfhFXzc7CDABdPHAZ456juPbQE8jeO5rQe44/KzQ+Wj8SN6vvPYFg75ETyZjHevzpoUtmFP8p3AkFsIL3rvdd0gz0wmPPf2Qa+YnJHBnK/HxaI5TZ7TOqOCc7vGRPngxk4e3rSbT54/JTRJvrKzh9bOo8H5oXjkT8aF32oez2/8lp2Ok9V4NHG1kjQ3LpzFo5t3BwLXJFofSpcJd5UKsq/7M555b8GM8YGTO9paNYqO+opiBhDceV18HaZqU6jl6B8A/wmYJyKvG281Ar+q9MAsI4ckvQY05TQJFdJUBpqzoM0/LTPG0959LLSa7PATv/Kx/71T1PkTmjY9KOD1rmN86f4NzJ/aGNr/2Tf288K2g3xl2dzA5xBNfqtLCV+8pJkbFs5i675efu6vWvHHGI266ev3CtzFOaFXf3VpUC11td/LwsxgLtTj4kBvbpy/SdpVrHtjP9FldMZVXPXhqUxqHMXDm3aHQ7AM9GHK/x605uDmzruxlWLNZ/CK+VOC1X0wDuX5fzoPv8+3P3tBTpnw/nS4BPl3H98ShHLrAI1o4EAxHDxNylWK9u5jNWFSilJIg1iFV0DvvwLfNrb3KqWOVHRUlhFD0l4DJuUyCRXSVEp1eK/a2BmKkTf5qZ+wdajIJImCW/zJVheZM0ssjKpzcg7pS7vc/9K7gTP78vMmexOtTyajmDFhdGDiMktqt3cfy0kCK9QPWt933bQnWto6X4+L1l09PP/2wZyxnzN5LDsOvR84Yl3/HsTcFnYfORHrWwBvIl08tymICjJ3Szm+xmJsi2oC0WfwE+dOjr2Oq+CeF3bQPHEsPSf6cuo5icpGSWWU57dZNKcpCNDQjY7SmWz4sUNWsMVdT5u6Vm3sLJh3US0KtRw9BhwDCia8WSyFSNJrwKScZp98mkrrrh72HD0ZMs8Ucnjr1WS+vsL9GcXqjZ3Up4Q6PxRTgE+cO4l3Dr5H99EPAK943g2RInNmpu85UxtzQiPN2HzXVUxpHEV9JBpIT4jRz2smYZlRN8XIJ8Dzbd+w43DgE9A0pITfu3QeK57YQgHrCymB598+SNqvpaR9IeYhE8bW85vdR3OOFWBhcxObdoXvWVSDMJ/BU/1+bwf/HvplkkLX01VV6+uyCXiOwMI5TaHvx/Tb6Of5xoWzuGvd24HgB88f4+pwMx9tsjJNXX1+ZvWwEBCW04dq11Eyx1HI7DOQQnJx9XP0NQS46oKp3HH52QU/94YdhwtW5ASCRK5PXjCV9W8dwFWKV/zKnPoccff3xoWzgoglyNbyCSZV47ouninnzs+1sH7rAX751gFcP3MdvFDjuHLZAwnxzSfAze2m+SrqSL/zcy30nOgrKBwAWmaOZ8ueY6EcEoFQNrjunQB+YyB/VhdH2OSbm0yiGsTSedk+0Qp47u2DwfiWzpvIzzv2hZL4Fkwfx6I5Tdy0aFbg0FYKzpnayKtdx0KalcZ8Br959Xkhwf+VZXODYoF1KYePzhrPqbTLsnkTeXnHYV7rypomo8EC1cYKiNOcSmcaRxORzNDH6DjuWvd2qOzBXeveDqJR8o2z2PijE525mgRY9+Z+rpg/peBnNvMXRDyhcvaksfzoxR3ZxDI8m/6UxlGhqqE6Eznu85rj1vbnB29fGlqBRifYdW/s58VtB7lx4SxcN9tfwSxvEpfQVm7hHx3/FfOn8PM39occ6QoCLY2Yekx1jnDLxc1s3d8RTKbfvPo8vvXoa7HXFAga63R0H+OhVzpjXRZRX9CiOU0hv0MmE9am9H3RSWs/fnknn1owzXNo+457cbyyGKYzGcgpta6fQfO+6wWGfia0FvJa1zGm+RphOqNyhE4tYAVEhahmF6hSqESmcVxDGsjfnjIagil4q+VfbT/Exh2HuXnx7HD9H2OcUfPBn/70VZZfdnbeYoHRshKuIlHtKF1O2gGunD+FZ9r3BiYEM3saMKKBwpVC9f02aw1FC9Rt2HGYa1qms3HH4WDFa6L9FQoSlTcx/QpxFUsH0mdaj9McP/54tCNdRzvVOZ4TfcGM8ax4sj0ILTWLOkYTLD/oz+RcLyVZbQ88M1C+iKeow7x1Vw/PbT2QPVcqV5vqPZUOTE1mbSWMvBXd+e3B25eGOtbp7wW8Z3DlUx3ccnH2+dNadFyP6n29p3DwtL+4NqbVxgqICjDQVXk1hEqpJqBiIaP5GtKYBf1ORbSDtUaDdwdonjiGziMnQqUR6uvCDWP0OKM/vp2HTwShonFCQkedmBnL5uQa/Yytu3r41qOvBZNRRsFfPL4lWLkK4IgEoZWtu3pwXTfITVjxZDsZPzvZcZyg6umKaxfkNP4xE8aiE4l3Hc/UISKMG1XHJ8+fwi/eOhC05cz33ZWifUFWkDuSvzJvNHvczAq//LzJQU2hjOs50U1Hrhn5pb+Trft6uWvd2yyYPo59xz/IuV5GwQvbDnLF/CnB86XvCxIOftI1nsyGRWZ3uZsWzcoRko9s3p31GfiC/a51b4d8BAovv+Xe59/hl28diA1tVXiawWtdW0JNm8ykySguBIEGtYYVEBUg6ao8X2XPSrYWjJvkb1w4i4O9p5jUOIqt+3pjS06bKyat5rd3Hwsid57beqBgQ5pt+3uzmcN4cf4vv3OYaz8ynade6w7eq6tzWH7Z2aHyEnqy/aIRAWSaqh68fSl/+tNX2Xn4RPA5H97UmTOxmZ/9rz9/YfB5dI/k6IR5ydwP8aLhbNSYk5H2PegWmmvauoIQVDNxKu0SxGSe6ne574V3+MqyuUGdJPOZicbo64lG27IzruKeF3Z4CVgp4eaLw6vP6HccfR51slt3JAlOb9Nlv10VX5lXn19HRenwV+Ufc+D4B9ncCUfoPnqS1l09Of4LrdFs3dcbyv/IR1+/y8ObOkPJfh8/x2sleudTHSHfQLRhUbQbnckaI4cDvKq3phCKYhbUK4T5G1j31oG8+6WEipSAKQdWQJQZM0ImnfYqRMY1aI/2UPjk+VOyE0RMxqp5XCEtI66uTdOYhmAy/+XWA2QyirqU8NHZE2jrPBrYsuMyPT2nYDgZ6YN+N/hBRzHr4LsKtu3vZflPNufEnYNnOzcLywFcft7knLBBHWm0wM890LV5zNDNzyyYFlqhdew9HkxK5v02BbCOX9c2fPM7+KDfzVs2O+czk/WZnIoxj2h0NIvWdO55YQff/8KFAHQfPekV7MuoUMG5Or8u0g1+2Q1dqkNfV6/Qzc95630v059R1KeE1cuX5az2TfNPyhFURgWr5h+ue9scMhlXcde6t4N+znEaInilsfsznp399a5j1KWEsyaNZdeRE6x+JVwcL/pdRPM/8iECb+w9njXtORKY9W5aFM5w/ovHtmQznCPd6ICQ2e/R1q7Qc7/ZcHyLf11TICjfoV4sqU/7pZrGNPBOpM6SyV99/sKa1B7ACoiyojMvvZWTX5dFxTdoN00uaVexzliVuMpbAbuKHJPArT/aEKyUorXizR+enox0rHWU/ozKqQ0Tt583CSW/Byryd1QAFOOXbx1g+U82B/V8tNBqmTE+p2hdX78bqiB7iRErr9xsrZ/oarkv7U3msz80JrAh6+9ArzSL/fijuBAKbdSYQrdpTD2ugiPvZ8MwH3hpBzsPnwhWt/OmhLOh5046M5j0tu7rzVm5Rs2Ca9q6Ag2kL6NY+VQHK65bEDhN9xw9GUTm6FWz9x27tHcfi+3Z8OK2Q7y47RACoSKBIVu9SFAmQ597+8Fs6Y++PH6j/rTL1HFn4EXU5yflCFedP4V1b2YXGspV/LxjHz96cQeu8rQprT1ETUZmLke05Ho0RFcLBwdoMJzij2zeHXxPl507OXbRo3EErr7ASwAslkDZc6JvSDP+S8EKiBIo9iWamZdpIyvI/HFooin60WgMnW1rmmnuff6dQO3tS3u20Ct8h+mC6ePo2Hs8+OG5pc5wNULGVaEfnp6UDp1/KqdonUQmqwl+uKWrVI5dX58HN9sg3qwoCp5gPnvymTTUOaHQw2JMOrOBQ+/1xQrYC6Y38sZer6vbofdyM3zNRLJ02mVsQyr0/vYD7/Gl+zfw4O1Lc+L7HYEV14br9kTDJF/zs7R1b4agZAdhYZ5xYfv+3lDhvWj5aUW4dae+x3eteztnko3iKgIzntaW9GR7x+Vnc8X8KTy8qTMIeTUR4JaLZ3PjwlleaK//Y8kouPeFHdkFQ0Zxz/Pv8EF/JhB+UZ9DVDgJhDLMTS6MNFjSWpw2ixUSEEoRjLXOEVJ+pFIcvSf785YRh6ExPefDCoiEmKq7I1789rJ5Ezl+Kh2sEs1YebNJu/5xmGhn6XefaA9+dNHMTf3kv7b7KD/42Zv8/M3wA/nsG/uDh7SQ7bYYcclJtYLWsH7h1wUys1Tx2zSqjBcTbzpK9Y/MDGlVGcUUvzewNs9EibboTIKpEZikHKE+lZsdPenMBo6835eTAYzAWZPG0r7nWEiD0eara1qmB30jNFGhoWsqmfbz/rTLGt9kuefoydCzaXIq7YbCM+95/p2cEtR6RW2am0yfgI7zb93VE/oMDp6fyqxtdMsls4PwXj3ptXe3ezkOZBsgab/BojlN3H7pWSFTYvRj/ML/jSiyGkDLjPGBSSkalKGLCD68qZPuoyc5aAjxlpnj84ZMr/FLhWvGNqQ40ZcJmb+CQoqu4pZLmnnl3SM599MRQgu7vnRYK77RbCpVpijDUrACIoY4TeGe598JVPeM0pEK2VVmfUqCH68jnk9Bx4U7hH/IumTBgd5TniDAU5dN9MuMouBKJSnTxo1i6rgzYlfGtSgYoijlrQRf2XE4MF2YTmDXVaS1QHZVMIGZuMT3Bh4MQvxkC/DVS8+ieeJYXusK+2vOiCmpgX8ebZJzxJtkXN8n8eK2Q2x89wjXfmQ6T77WjfLNj3HFDFd/NVt6XAtMXQKiLuUE0VBR39ItFzeHJsGoNnLOlDP52xs/AngJfc927AtpdRf6iyYvKcwTkIJCKc/M84s39wdCI51xmTnBq/l59/rtbNvfyxOvdoe1G5FAc9BjahxdHwi4OP+A+feHzmxgwpiGIJs7LkcBCIXfahzx/Gd/8diWkHlKHxe9N+/3ZYLjrr5gKlfMnxI4zlMpJwgEyen5kMr2qzD7d2vrgYLEUYaVwAqICHFdo9q7jwUrk3z0ZxSf/vBULpo9IfgSX9h2MBTGqCtw/jLS8GQo+MOrzmP+tMac5vT5uGjWeG65uJln2vcOSjspGwInTqV5J1LSWhOaJPBKSb+6+2i+2m8DxnQ06ygi3cPAHIeD14+h50QfF0xrDGkmZ9SnggSyOFOOPs/i2RM4lXYDod6Xdnnq9b3BOKLmJY2e5LVJxCy+p5sVeU2HHL566Vw69h7nmpbpzJ/WGMqVMKvBAsybNBaAW3+0ITbC5429x0Paj1IqVOhv9cZs+XMdOWbmv0RxXU+TWNPWxdq2rtj+2IV8AYfe6wuZ9aKJi627elj5VEfOb1EL/Vd29vDKzh4eae3i9z6WjSDTUXxxmphScNHsCcyf1pi1GyvF1n29OfWqxH9v/rTGUDLpyqezEVk3LpwV9AWxPogaIBRu6DtBk1Zp1OWZ9Ze44toFgX/gu09sKejsdUSbqYpfZ86HxrDryIniO/rnXf6JeUHI51eWzQ3ZbQsxf1ojnYffTywgdHXKSmCurpNQrN1jKeh2lmZZZrPgHZDz446u2s0qru8cfJ96owrrmrauHAEB0Np5lKvOn4LpwNVmMUEVrVoKXnTU2/uzzm3TtJbJuDSOruf//P6S2CivlhnjQ5Pgc1sPMLlxVJAYF8VsggSeEGgcVceGHYdZMH0co+qdYHHSMmNc0Ao037OogIde6QwEziOtXaz+algD0BVskzzP0XpccYslkVx/YF/a5T7fEQ7evNBzoo+rLpgaqp4LXnXdPUdPsratK+i0l3EVz7TvDflptAU5nVFBTpDO7I7rzFitKKeKCQgReQCv0dAB3dM68r4APwQ+C5wAvqKUavPf+4z/Xgq4Xyn1g0qNM4peofQZEUZJSftRIwtmjmfcqLpgxfGr7YeKTvwZBdMaR7GvSEVQR+COy8/mua0HkpmeFLxz6H2+/D83MnFsg2eeSPBZXus6xm/f+3KO6asQE89sCNlwRwr6d22WaNATMYRt0/rHHVcyu/PIiSDSKRqe+rAxEWpcV3H0RF/WpCJ+S0836yD+i8e2hGo5mU75YgsOnVEcLXOicyXWtHWFjtcLpai/TGtVukaSNi9d+5HpoY56H501nld9bejVhEEA0Y5yORrA0x2JnueL5zaxqLkpSMbr2Hs8Vzjg9fA2S6jo7VFfUdOYBr52+dk8v/VA4BCf0TSa/ce8nJA6R0Ja4mhfa0xnPDMSyouUM82HOioxXx2salBJDeLHwD8BP8nz/jXAuf6/JcD/AJaISAq4G/gU0AVsEpEnlVJvVHCsAYvmNLHi2gX8pZEtmxSXXN8E5C1xn0Mx4aDtniuf7mDFtQt4YdvBouYiF3JWOUnJV700H0MlHOJWeZXE8a9pmgp/unk3GSPXwFzp6ckrWjIbCBVx02U4msY0MH9aNtpJowhrQsq3u//2Jc20REpXPOqXHA8V+iuAju4BcjrN1dc5Qca7uX99ncO4UXW5ZblVdqWso8VWXt/CM+17Q7u9sfd48YFFxihktVKRrOlwSuOooPxKEto6e4Kw7rwasf8Du+qCqax7Yz8uWT9HKDlSeb/BB29fyurlywJ/T3fPyUBYeY7p2YCXZ7Huzf1+9Ff870pHJd73u4sTfZ6homICQin1gojMLbDL9cBPlJdZtUFEJojIdGAusF0ptQNARB7y962ogDBV847u3FC7WkDhPZx9/S4d3cf4yMzxbNrZMyyczOVkKIUDEEQXfWhsA3c+2Z5TolnnGkDW1NTRfYxPnDuZKY2jAuGwpq2Ly86dHOR46ISzUp61dEYxc8JoL3M9Mg5vgs/WmSpEypGgb7Q28zhkM5PXbz0Qeq6u/vBUrpw/hRVPtIdDjcnNfdE9thdMHxeajPtL9LupyLm1X0BTn5LE1U+T5PIo5fWDcARSqWxIdPR2ageyrjjQvudY0Go2GJvvP9Cl0F3lRdEV4udv7OcHP3szyK6vBS2imj6ImcBu43WXvy1u+5J8JxGR5cBygObm+AJtxTDtkYKX0FRuTPW6GPnCEDUuhOLZC51HKJ7xORjGNqRQKE70Vcr7MDA+/9EZjB1Vx8HeU8FqcKC4Ci9y6mC8g/y1rmPc+qMNuErlODxTAo2j6njgV++GymHrvI5837PpDI+ybX9vTtCE/q5vv/Qs7n/p3aKm0bSrAk3UdPpe0zI9CF01z/3R2RPoOdGXe14hlAHuGJrWf//lttCu5X4M0xlF05h6jpzoL7pvvt9UNIAACCbz6O51KcGBIDLsET97PEo0gMDMYsd/RrR2EhWuuoSKruFUbSFRTQERJ/xVge2xKKXuA+4DWLx48YCewQ07DgemGgWJHrhSae8uTb3WCLk2Xyj8Y0s5wnUfmc7h9/s41Z9J7LB1hCDMLik6vK/WGDOqjr/xy1h886HflJzRXSr5TB0ZBff6ZaSDbX4yoBRY/l73kek8/fpez2xjTG6KXGe9fkZWv9JJQ50TCIliwRWn+l2ead/LV5ZlI5nau4+Fooq0yejZjn2MH527cFLKEx6Xz59C78l+OvYeZ8H0cUHNpFKITphF9xc4ejL3txqX13PrJc1sjOQhiMBvzWnirX29oX11GHDceXUfie6jJ3nQiMoKobIBBLpWmNmq1gxy6D3ZH3KA63FXI+chjmoKiC5gtvF6FtANNOTZXjHiaiWVm2IrulF1DqfS2VDJlJEkVCytP8qi5gn8a8e+2B/oBdMa2d1zgvdOhSf2lOPFnI8bVZe36uRw4uFNnRzq9RLiflmgUNpQkM/iU8gS9NTre7MaRJFZ8+K5TWze1RM4mDv2Hg/VbMo7LrJlNByBjTsOh8w6dQ6cO8VbYRfKLD/yvlewT0dtRW38xTRigDMbUrxXZLEx50NelV+9ijw/4rcR8cxkENaa6xwJEuLMGmIXz2li3Ki60H3Smucz7XtDjYrA01h0kMKqPMJB+2qi+Qpr/EZQcdnQzRPHhqIl850jH5UsxVFNAfEk8A3fx7AEOKaU2isiB4FzReQsYA/wReC2Sg2idVcPK55sr9TpE3MqsgI9b2ojkxpHsWD6ON459D4pX4swnWYisHhOE70fpENqcj6/RF1KeOfQ+7FhihlXsX1/L6PqUyWv5GqRjFueBMO6VDZZbShxfTOG8vsrhDLII5zyK5ZmMp4Z44z6lGeiKmBbjDpeXRX2EQgwd+JY3tpfPLN855ETvHPw/bzPTBKFtJhwAJgwpp49xyQIp30z4tS/eE4T50xt9BIEdUtWPLOb9g2ljOTATTt7aOs8Ghzv4GnET7++N3ZBZ07a7TH1lepSwi2LZ+ck1u2JqZprTuQ6BF2X6imU4xKl0g2/Khnmuhq4ApgkIl3A94B6AKXUPcDP8EJct+OFuf5H/720iHwD+De8MNcHlFIdlRrn2rauIU9aS8Jb+3qR/b05qzHtqAZv1f+tay5gw47DvLVva2yNHfB+7JeeO4nmD40p6LswTVG6zEGhH7c5ycRltQ53LpnbxLeuuYCt+3r57uNbglXpUAjQupTwX3xzRtOYrHM8GtkDng+kzvGib57beoBfvLm/qPN25vgz6Doa7rtg9qwI/C5FOGfyWHYcej9kkqrUvTn0fl/otxq9TuuuHiaMaQibSAUe+PVOL7yUsGahI698f3TBiL9p40bxh1d5lWPvXr89KHOv+dDYev7s0+cHk31cufG0q3KqO2sh0n30ZBBcoFR8jksclWj4ZVLJKKZbi7yvgK/nee9neAKk4tTqfGYKgnyk/SbnOqEpKuf0JDGq3gnKIj+8aXdRc5eDVzahY+/xvKvQOgdWXn9h0FoSCGXKjgR+s/soW/f10nOij7/6/IW0dx9j+/7esibhjR9dx7GT6ZztAkEF4LvXbw+t7i+cNZ4p487gjb3H2dNzEvCKQ+4//gFpP/KmmICICgfwtNb6lMPh907lvO/42qo2Zeltv3fpPO58sp1+vynSLRc3s/vIicSl0ktBf9Z8ZJTXklUMJ4Kub5QP5e9TbB7Yd/wUf/l41jxV50jQKlQBPe/3h6o2mxN3JuNy1QVT+eVbB0K9Q4BQkyitBZZiXiql4ddAOO0zqW9cOItHN+/OadJSbgqtyAWYOSG8otPPeLFRHeo9FSQLOf7J9HEK70E21dWV17eEVsMpI7lJn6Oh3gmayevx3bakOShqNmXcGXzNb/1oZhOvbesKEgxrVfCWQn9GBYXTxK+vtedo4UmqVI6dTAcrWPOe9UeSLk3t8LWuYzSkjucI+qnjzuDNfb1B7+xSw4GjeRjgLQQ+ef7UIDT3N7uPBosGV0Hn4feDypIiwoHeU2x490jVzJQuMGfC6JxKA4U03KTjNI9Nu4qrL5jK2/t72Xn4RI5jOdqDY//xDwK/kNliNhAift7ETL9nRVItwOz5PtJ8EDXBojlNrF6+LGhOowtklYoAH/HrFz3+m66QH8ARQnWdtDPPVdkJ+T9deW7QBlPwCn7tP/5B0bLTR0/0BVEnKYFbLvFWcDpjN6quxrV+hGz8vjnhRxO9Fs1pilWhte3TLC3ywK93Jk5iqmWCSVipASccasaPqePYiVxtIaOgISU57S3jki41cQuaD/ozQe/sqHBIOV4Ezetdx0p6vhc2N/HCtoPB9zznQ2NCpqfHX90T/Gb6M4O/R+UgrgxN4xl1vHcq994PBt1FEbLNgfRvx4xeemTz7uC+6zBgvZ+5+jeLEpZCJTOvT3sBAdkbfKNfF+fhTbtLziKuT0lQO/62Jc2hHs160jVbfEbfM5vBKDwHa12qmKHA737l/62rRkI2Y9ds+QgEKw3dycy8B1EKrUzi2liu8XtL/3r7IZrGNsT2P9DMnHAGe2LMHCOZOOGgKYcGW8isk3Icls2bWFKfC/BCvnUIeF+/y1mTzwwJiAN+pFitc/yDgQkHEfjEOZN4cfuhkNCdN2ls0MtDCPeO0NWa9e66JpNORNS92KHwbyyOoW4eZAWEgSko/vaZN9m0qwcM1T/l66muH5uuTTkC3Lx4dt7a8fmuY3JXpNUjEERimERVdy1UhHBjlAdvXxpoRatf6eSR1i5QyrcVZ5vRF3rgCn2GqO1TQbbFowo3x3HEaySvS207wIdnjD/tBEQ1SWe88NdSTD+OwLuHsnkDOrnra5fNo2PvcUbXp0rSGD6UMKmtllAKXtp+KFSjKSWehq+1ZC+iystz0l0ftfZc57cB0M2RTOEApa3+Kx2xFIcVEDEsmtPET7/2sVgtAHIrd5q1dgbKNS3TQxFLQriUtLaBXzl/StYpaGR2Rpuxa0eZNmWZpjPdgxnI6S88UNsnwE/zOMAV8PmPzuTHL+8M7ldx3aj2mDxMihHG+TQckaDvQLR+1zmTx/J7l86jvftYUGPKEfikX5PI5Odv7Ke+zuGmRbNomTGeX7y1PwgbLVbNd7gJB42rvNLtf/X5CwOf1AO/epdmw9yWcVWota0mk1Es8IMKpkTKp5dKpSOW4rACogD5pHu0cmc51D1t29c2fF2PBci5hnld/X7TmIZQdVEgx1GWybiBc9pVKii3PNAHLnp/Vl7fEvhRTJSC+196l5XXtwSC9t7n3yn9JlURAWZMGF3zAuIi39Txw3VvByanlBBojPOnNbLyqY6QqWne5DPpOdFHy4zxPCpdZPByL66cP4X1b+3HdCUpPMeqjlgzv+op40ax//jwMDlFmTCmnqMFBJjgBWToFrd9Rs9t7X/QTY9MFPj+h2MIXvnyqy6YGmoLkJRKRyzFYQXEICi3c+i2Jc2BoIhep9h141TP6Cp/677eUDtDs5NVkgcunznK1LTqHIm1p6ddFSqV/VykeUoSBAr29q0kCmJLTdQao+oc/vaZN8NVYPEWFa27erjn+Xc4/F44hn+d37pWjP11afNPnj81NuEw7hsod7e+wZIkg1ujhUO+cjONo+qCCTqqgU2fcAYzJ4zOKYEybdwoDvSeCvfiUJ5/8bm3DwblvVdt7OSZ9r1c0zI95/cf/c1VMmIpDisgRgCFVE9TmCya05Sj9RTTgszJP84cZdpFHeOHFWfr1glC2vQVZdq4UVw4awLrYhrApAT+6vMXMn9aI2vbukKNZAQ4e/LY2MSuUiaJYryw7RCf/+gMNuw4XHOToSYui95VXgjyw5s6iQss05vM40QktijgcGJ2U/LGWmDk/8RkSf/opXdpnjiWGxfOYqPR9hZgz9EPcvxpgtfFMV/FXh3Yce/z74T6yncefj/oc79gxvjAnGyWlbc9qS0lUYrqGX3ACj1w0clfq9emEDKFk9fG0i8P4QgZFa59/0z7XuZPa2TpvIk5Zakdgbu/tAiA598+6JvFhE/On8KkxlGhEMBFc5pYMGO8pw25ioZ6h6svmMr2g+EaUjMnnMHXrzw3COvt2HMsNsxTZ5pf0zK9aIvV9j3HQrb0cgqgYtQ5xE7wJvmG0r7nWNFjTdKuylvgsJSOhnFMGF3H0ZjkwHJTqnDQ+T+vx0R6ZdxsTkxdyinq7L94blPIbDxxbANPvdadXdRIfEVmsw6a+Wz1+UmxtuWopWQqpXqGJn+l/IYnKiSEosJpxbULQqG7ZhGyl7YdYtPOIzx4+1IvYc9/D7KJhEAQxO9Ajq3WVLkfvmNZ8PeGHYdzfrR7jn7AnU+2s3r5Mr7/hQuDCJNoLSqdaa41qpffOZw32/xk2o3VfoaCwaSVlBreWojdPfkn3jPqHD4oMtBiwiEVUxUAYNwZdQMOVy1EnZ8BrgNN4qoNiGQ76vWnXc6odziZp1qtI/D535rFqo2dIZNuqdn4MZXVhxwrIEYIlVA9C03+5mo+n3DSE+5d694OEvfMtpHt3cdY7a+ilCJwspu9fE1zWVyYn+7jC95Eb5aqBi95yzyH64bfTwlBprkWPrpctqtUTv2eBdPHsf/YB0bdHII6O8OBUXXCqfTgxlroo048s4H9x08N6n7ECYf6lDBzwmiO7ytePLBUXBc6fW1j0ZwmVl7fEixsdEl2M5xcQUg41KeEmxfPDtoMu0px51MdoaCQvrTn0/n+Fy7ky/9zY+w4UpLbJlZXVChHpORAsALCkpekmkmxnI9vXn1erDP8xoWzWGtka8dll5rmsmK+Fp37YTZyqU9JKNoruvh3lRedEhU+OuKqaUwDdz6VDWe+4/KzmTdpbFDDX1Fa3/JqU0g4NI5K0XtqcP09xp1RDyJF6yaVwsym0Xz9inP4+2e3Jj4mqk06/oa4Nb+Lp91u3HGYm/1qrFo73XPU6zENnkY7LhLtNHfiGP7htz8a1MwyzbDRMGP9HEZD2j//0RmcO7Ux0Lq/60cC1qck6D9RrQ5zVkBYClIOzSSfoMm3PZ9QKuZrMRMddSar6btYOm8i9ZGSFlqARIWPjriC3LDiH7+8Eyhv5dJzJnstTctZCLBUBiscHMjpzlYO9h49yfd8Z21SontefYFXT2r7/l5ad/WgFDgpL6hCJ7z2ZRSrNnaypq0r0E51VrQOFT93yplBb2uA5ZedHXq+4rrHOX5iqt7P9E1EI5eSBI4MJaKGvMFv5Vi8eLHavHlztYdhqSCDLTXQuquHNW1dHOo9FXJ+aw1CC598SYN3r9/OPzy7NcfM0uCv9jq6j3Gg9xTrIz2jAb7kFzx8pn0vZ9SnQtFaQtYpOZx+kQJ86sNTuWj2BJ7t2Jfj60g5wqyY4nlDNTbIrsR1RFFdykv0E+IdxSmBP/n0/GCBoIXEI5s934QjXoTRLRfnhqWbzyfk5jDVIiLSqpRaHPee1SAsw4rBajSFkh+TmNP0KjHalvPmxbNDk4XOOfjlWwdQfqSVWfCwdVcPL247GJxHx8hjnFOAurrCzYKqjSPZQIKmMQ281pUtif2pD0/lyvlTWPl0R9Aze+yoyjia9VjM+3THZfOChNNo+W1dNfWRSCXnaNE9MKoSGKXUP71gWt6cpagfbjhjBYTF4pNE+Ji+jkdbu4L6/VEH4qI5Tfzodxfn1Xj0efTKNJpc+JFZ4/n0gmk0jWlgxRO52em1ghjhZ3Gmk7vXbw8mZkcGXjSv6DiAv/78hTy39QD7j38Qu7qPmicXzWni5sWzAy3CkdxiepqkoeRDXUyv0lgTk8UyQMo1GWhtwyx89/0vXMhtS5r5zmNbQv2PKxXqWQqTzmzg8Ht9QYl50xwTxQwtrnTHQX3PimX8m9uTmhbzHR99f6iL6ZUDa2KyWCpAuUKLtbYRV3IhGvv+XgLhYJbMMLclnZvrU16yY8YFx+9C5fq2FaWylXqjvQ3yYnR3qyQPb+pk/rTGvJN03PdVSg5Rse+7GsX0Ko1T7QFYLBaP25Y0839+f0nINHLDwlk0+NVvU44kmuRvW9LM1R+eGtomUUmTh4tmjefmxbOzznIFV50/hbOnnJnThOjCmeOLrpK17X4o7BRTx52RM0mvaevi7vXbg34oUcppEtJmqFRSwTkMsBqExVLDLJrTxOqvLg3Vw4omA5qkHOEGvyGVjpISsh3mBK8MRFvn0dj8jQUzx4fyU1Iph+e2HogtwNgyc3zRSTXOqV8J6lPCHX4b3CDU1JGge2OcyUebhE71u6EeKQOlGsX0Ko0VEBZLjaMnmrVtXVx27mR2HHwvtjAh+E2t8CbmUfXZiVL3FXFEmDCmIcgENxWL+pQEYb96ojMTxULXcUiU2avP9ac/fZWdhysT6vqlJc1BhBhk82i6j55k9SudeU0+G3YcDgSX7pEyf1pjRaLkhqvzuqICQkQ+A/wQSAH3K6V+EHn/z4EvGWO5AJislDoiIjuBXiADpPM5USyWkU60S1lKvE5lmYzCcWDymdk+DLo8ydevPCeYKJvGNLB+6wF++dYBMq7iua0HqEs5pNMujiPcfulZQTho1F5vJoqJwNxJZzJv0tiS+hksmtPE8svO5juPbSm+c4lcMreJv4lpn6vHviYmU1+zdN5EUkaZFFepivgNhqvzGiooIEQkBdwNfAroAjaJyJNKqTf0PkqpvwP+zt//OuCPlVJHjNNcqZTKX1rTYqlRyrli1HZ1TUbBp+ZPYUrjKB7ZvDsQDlGnsb6uNqNoE086o5g3eQzvHj6BqxQ/fnln3kmrXGaT+dMay96HPOXAt665IO/7xcZu1l3SBfUq4TcYzs7rSmoQlwDblVI7AETkIeB64I08+98KrK7geCyWIaHcK0bdR8PkwHFvou3PeA5gh/gYfj05mfZ/F3jn4PvBtmKTlrkiv3v99pIFhWnrLydXnT81Ud5KoX3mT2vkty+ejUDITFVOqtEJrlxUUkDMBHYbr7uAJXE7isgY4DPAN4zNCnhWRBRwr1LqvjzHLgeWAzQ3D9zBZLGUi8GuGKPaR8+JvpwwVd3GUuM4FE3wSjnCBdPHsWXPsVB10qTdBAcq9OKEVCk4AjPGn8Gxk/2helGTBtnjOfqZkvhUBqIZDmfndSUFRFxgXb5n5DrgVxHz0seVUt0iMgX4uYi8pZR6IeeEnuC4D7xEucEO2mIZLINZMUabNK28viVwOGszU1y9JpWnW0B0cgKCxLCUI0H10mKT1mCE3tJ5E/O2ok2Cq7x2pmbUVYPvUB8MpX6mYkKykPAoV87MUFNJAdEFzDZezwLiW1TBF4mYl5RS3f7/D4jIY3gmqxwBYbHUGoNZMZqTlqu8yJqH71gWcjjf+VRHyCcB4LqKtXk6jkUnp4GMTQu9vn4XRHhu6wG6j54MCZdCZUWumD8ltrd1EkTCJdUdgTs/18LWfb3cte7t2F7OpXympIK8kEAZzo7oQlRSQGwCzhWRs4A9eELgtuhOIjIeuBz4HWPbWMBRSvX6f38aWFnBsVosZWWgK8ZoO1bX9VpN6uJyi+Y0hRotaRTwyObdibSBgYxt0ZwmVly7IOgCuGlnD5t29vBIaxerv7oUIHaC1EJjMJw/tTFURtxV8NhvuoKy27q3QqlColRBXkigDGdHdCEqJiCUUmkR+Qbwb3hhrg8opTpE5Gv++/f4u34BeFYpZQZ2TwUeEy/9sw5YpZT610qN1WKpFUKRNa6iLpVN9qrzTUItM8YHOQ6IBG1box34yoWe5F/dfTTUYxyyk+GeoyeDSKm+fjdY2Qcltp14E1gxHIG3YnpMbI70zXimfW9BAVFIu0lyv/TxcV0VYXg7ogthi/VZLDWInpDMZC/wHHuj6rPtX3V2dZJic0muF1fgLhoma9JQ5/B7H5vLj156NyQ8HCHQhHSJ7IY6h1ODaaxdAF2oL47Bmn+SHj9ck+FssT6LZZgRTfYy+0bojne6z0G+VW1SCk2AcRFIApw9eSxL5k1kwYzxQf9m833tQ9EoGLRwyFdwMCVeuGo+ouaftW1dsR0Hkx6fT0sbro7oQthifRZLDaNt/6aFJpVyaBrTwJfu38A/PLuVlU93DGrVGjcBarTpRE8UjngazN/edBF/84UL6TnRFxIOKUdIpQZmTiqGrisVxVUU9HMsnTeRupRX8NBxhIc37+bBjZ2s2tjJrfe9nLeQn3n8SCvClxSrQVgsNU7Pib5Q97qbFs2i50RfMKmf6neDCKaBmDni7OfmeXSDpEO9p5jcOCrkCA9CcPvdoPdye/exUA+LchKnQSjikwnDO2Wd/ma0bX+muN9mOOcxDBYrICyWGkVP0k1jGkIT+I0LZ7F1Xy8iXoMGhdcLYcGM8YFTuBRbe75cCX2eFdcuYG1bVyihLCpA9Dh7TvQxblTutPKpD09l2/7eihTsc/CEaD7MkuPRArYph8Td4U4nwaCxAsJiqUGiCXNmQT2AlU93hEw7adcTEgMNtTQnQLNNaH/a5Zn2vTk9FkyB8eDtS1k6b2IwXok0n/jUh6fytcvP5o7/Gx9AMrNpNN09J+P9C47XrCiVEgRvxQ9esUIHL3KrmNnH1JDEj/rS5qpbLm4eUd3hyo0VEBZLDRJNmPvRS+/y0zuWAXDXurdzEuXAa5izdX/voEMtoyana1qms2nnkeC1QKzPQm8zOws1pIQr50/ht+/9NZk8PupJYxvY03My9r3rPjKdw+/3cU3LdOZPaww5l/V9StIJztRyzKivfOU1zPuvw3bjSpmMdKyAsFhqCF1e+0DvqdD2jKu49/l3eGHbwSCiyYzqaahzuOPys7nj8rMHbSuPs7nPn9YYMkHFldGONgYS4ObFs2nvPpZXONSlhFsububNve2xpTiefLUbF/jVtkNc/eGpOWXGB6IhmZ8l3/Fm5rgL/Gr7ITbtPHLaaRI2D8JiqTKmr8EsoxEN67xo1vig0J4DfPzcSSyYPo6OvccHXG5isGM2J1kt3B7ZvDsw/Tx4+1LWtnXxYB6n9SVzm/jp1z4WhPMe6j3FL7ceIJ2nblNDncPqr5Y316PQ/nete5tfbT+Eq7xw2j/59Hy+fuU5JV+7lrF5EBZLjRL1NUTbgKbEc6zW+yvtrfs7QqYf7ZTetPPIoLuhlUKc01Zv0y1PzYl41SudOT2twfv8qzZ20nOiL8hJ+IvHtrAqUkpEk7SoXpzwKtWnsGhOE9+8+ryQee10CnEFKyAslqpi2rpRipSTLZ1RnxLu/FxLThLcM+17uaZleijUtVSndCWzfqPCY9GcJu74xDzueWFHzr4ZRahhz4O3L+WGhbNY4zvBo1FHxSbpfIKgWLJboVIcp2uIK1gBYbFUlahDeMW1C+joPpaT5du6q4fvPLYlqMu0aecRPrNgGpC8p4Nm1cbOIPt5VP3QROh8+7MX8Is397Mtppe21pr0xG22S+092c+PXtxBxjfx3HndgoJjzScICtVKKqZdnK4hrmAFhMVSVZKsUOPqIZ3qd3n81Wz1/K8sm5soUa51Vw8rnmgPJuW+/sE3NErK5xfO4u/+bWtom+CFsiqlctqlLprTxN3rt4dMTYXyHSB/0bxC93mkVmItB1ZAWCxVoJQkrGg9JNH/MWbOl3ccTqQZbNhxOFQjyXFkwA2NktryTSf8GfXZyCBHCBLx8tWSKrVKaiFBkO8+j9RKrOXACgiLZYgpdZKNtg29efFstu3v5RWj5PWoOieRZhCEbxod6wba0GggXdjMKrRJCgwOxAdQqknodPczFMIKCItliCl1ko2bwFp39fDF+16mP6OoTwnnTm1ks1F0Lp9mMNjJMCqsuo+epHVXT97zRD9rz4m+mgwTPZ39DIWwAsJiGWIGYtKIiwx6aPmynOS1JJrBYCZDLWDWtHXxaGsXq1/pZE1bV14tSFdS7U+7pFKlm29syYvqYgWExTLElMukEZ3oh8pMosNG05mEWpD2eRRIys3n+E6ibQ3XRj3DASsgLJYqUAmTxlCaSZJqQWYl1f6MCsqSmxTSEopdx2oYlcUKCIvFUjJJtSBtYtJRWA9v3p2T41FISyh2naT+HKtlDAwrICwWy4AopLGYE/JNi2ax2i+dkc4oVm/s9Ooz+av9YlpCoesk0WSsljFwrICwWCxlJS60dVS9k9NXW5cJH0xf7SSaTL6WqlajKE5FBYSIfAb4IZAC7ldK/SDy/hXAE8C7/qa1SqmVSY61WCy1SVxoq67qalZ61X21B7uyL+Z7iWoZ5bru6UDFBISIpIC7gU8BXcAmEXlSKfVGZNcXlVLXDvBYi8VSY8SZfeIqva5p6wq0ikqWuIhqGba0RnIqqUFcAmxXSu0AEJGHgOuBJJP8YI61WCxVJEm5i9ZdPTza2hVUC0mVWPJjIGMyx2FLaySjkgJiJrDbeN0FLInZb5mIvAZ0A3+mlOoo4VhEZDmwHKC5eegaplgslvwkqS+VzmQbI928ePaQreJtaY3kVFJASMy2aKZMGzBHKfWeiHwWeBw4N+Gx3kal7gPuA6+j3IBHa7FYhoyoGSpfb+hKYUtrJKOSAqILmG28noWnJQQopY4bf/9MRP5ZRCYlOdZisQxf7Cp+eFBJAbEJOFdEzgL2AF8EbjN3EJFpwH6llBKRS/Ba7R4GjhY71mKxDG/sKr72qZiAUEqlReQbwL/hhao+oJTqEJGv+e/fA9wE/IGIpIGTwBeVUgqIPbZSY7VYLBZLLqIKFNAabixevFht3ry52sOwWCyWYYOItCqlFse95wz1YCwWi8UyPLACwmKxWCyxWAFhsVgsllisgLBYLBZLLCPKSS0iB4FdFbzEJOBQBc8/nLD3Ioy9H1nsvQhT6/djjlJqctwbI0pAVBoR2ZzP23+6Ye9FGHs/sth7EWY43w9rYrJYLBZLLFZAWCwWiyUWKyBK475qD6CGsPcijL0fWey9CDNs74f1QVgsFoslFqtBWCwWiyUWKyAsFovFEosVEDGIyGdEZKuIbBeRb8e8/yURed3/92sRuaga4xwKEtyL6/378KqIbBaRS6sxzqGg2L0w9rtYRDIictNQjm+oSfBsXCEix/xn41URWVGNcQ4FSZ4N/368KiIdIvL8UI9xQCil7D/jH1558XeAeUAD8Brw4cg+HwOa/L+vATZWe9xVvBdnkvVlfQR4q9rjrta9MPb7JfAz4KZqj7vKz8YVwNPVHmuN3IsJwBtAs/96SrXHneSf1SByuQTYrpTaoZTqAx4Crjd3UEr9WinV47/cgNfxbiSS5F68p/wnHhhLntawI4Ci98Ln/wHWAAeGcnBVIOn9OB1Ici9uA9YqpToBlFLD4vmwAiKXmcBu43WXvy0fvw88U9ERVY9E90JEviAibwH/AvzeEI1tqCl6L0RkJvAF4J4hHFe1SPo7WSYir4nIMyKyYGiGNuQkuRfnAU0i8pyItIrI7w7Z6AZBJVuODlckZlvsqlhErsQTECPV7p7oXiilHgMeE5HLgL8Crq70wKpAkntxF/AtpVRGJG73EUWS+9GGV+fnPRH5LPA4cG6lB1YFktyLOmARcBUwGnhZRDYopd6u9OAGgxUQuXQBs43Xs4Du6E4i8hHgfuAapdThIRrbUJPoXmiUUi+IyNkiMkkpVcvFyQZCknuxGHjIFw6TgM+KSFop9fiQjHBoKXo/lFLHjb9/JiL/fBo/G13AIaXU+8D7IvICcBFQ0wKi6k6QWvuHJzR3AGeRdTgtiOzTDGwHPlbt8dbAvTiHrJN6IbBHvx5J/5Lci8j+P2ZkO6mTPBvTjGfjEqDzdH02gAuAX/j7jgHagZZqj73YP6tBRFBKpUXkG8C/4UUnPKCU6hCRr/nv3wOsACYC/+yvFtNqmFZrLETCe3Ej8Lsi0g+cBG5R/i9iJJHwXpw2JLwfNwF/ICJpvGfji6frs6GUelNE/hV4HXCB+5VS7dUbdTJsqQ2LxWKxxGKjmCwWi8USixUQFovFYonFCgiLxWKxxGIFhMVisVhisQLCYrFYLLFYAWGpGURkrojUROifX3nzaf/vzxWp3vpRP1M43/uLReQfi1zvOwMfbeg8c0XktgLvnysiT4vIO37Jh/V+Brx+//N+dd63RGSLiHzeeO/HIvKuX5G0TUSWlWPMltrFCgiLpQhKqSeVUj8osMtHgVgBISJ1SqnNSqk/LHKZsggIYC5eYbi4sZyBVy/rPqXU2UqpRXjFBef5718E/D1wvVLqfOBzwN/7VQM0f66U+ijwbeDeMo3ZUqNYAWGpNVIi8iO/Zv6zIjIaglX6Bn91+5iINPnbnxOR/yYiL4jIm34vhrUisk1E/lqfVER+R0Re8Ve/94pIKnphv6b/WyLyEnCDsf0rIvJP/t83i0i7X4DuBRFpAFYCt/jnvkVE7hSR+0TkWeAnEW3kTBH5X/7q/HURuVFEfgCM9o9/MGZc74nIP/ir9l+IyGR/+zkiss4fS5uInA38APiEf64/jpzqS8DLSqkn9QalVLtS6sf+yz8Dvq+Uetd/713gvwJ/HvM9vYCXRW8ZwVgBYak1zgXuVkotAI7iZWoD/ASvEN5HgC3A94xj+pRSl+FVUX0C+DrQAnxFRCaKyAXALcDH/dVvBm+yDPBX1z8CrgM+gVcmIo4VwL9TSl0EfE555Z1XAA8rpT6qlHrY328R3ko8upr/LnBMKXWh/1l+qZT6NnDSP/5L5DIWaFNKLQSeNz77g/69ugivR8levJX9i/65/lvkPAvwCujlYwHQGtm22d8e5Tq878EygrECwlJrvKuUetX/uxWYKyLjgQlKKd2F638DlxnH6BXxFqBDKbVXKXUKrz7ObLwKmouATSLyqv96XuS65/vX3uaXg/i/ecb3K+DHIvJVvLIK+XhSKXUyZvvVwN36hcr2FSmEC2jB83+BS0WkEZipvEq6KKU+UEqdSHCuAF8TaxeRtXoTuVVIo9v+zr+Hy/EqGVtGMLYWk6XWOGX8ncErjZz0GDdyvIv3jAvwv5VS/7nIeYrWnVFKfU1ElgD/HnhVRD6aZ9f382yPm4RLRRFfYroYHRiCVSn1BRFZjOd30O8vxqsXpFmI1wlN8+dKqUcHcG3LMMRqEJaaRyl1DOgRkU/4m76MZ2pJyi+Am0RkCoCIfEhE5kT2eQs4y7fjA9wadyIROVsptVEptQI4hKeh9AKNCcfyLPAN43xN/p/9IlKf5xgHr/AdeA7ol5RXSrtLRxmJyCgRGVNkLKuAj4vI54xtY4y//x74zyIy1z/nXDzn+T8k+mSWEYcVEJbhwn/AM2+8jhc1tDLpgUqpN4C/BJ71j/85MD2yzwd4ZpN/8Z3Uu/Kc7u98B3M7nqP2NWA98GHtpC4ynL/G6yzWLiKvAVf62+8DXo9zUuNpIwtEpBX4JNnP/mXgD/3P9Gs8v8nrQNp3XIec1L7J61rgayKyQ0Re9u/LX/vvvwp8C3hKvA6BTwH/r2Hys5xm2GquFkuNIyLvKaXOrPY4LKcfVoOwWCwWSyxWg7BYLBZLLFaDsFgsFkssVkBYLBaLJRYrICwWi8USixUQFovFYonFCgiLxWKxxPL/A7R4H5NY0AR4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is the correlation of HD area to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )   #need to calculate HD area before do\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()\n",
    "print(\"... and here is the correlation of tract usage to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e131623f-1b84-4a2a-857d-f46d99aa9c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.525 3.17963\n",
      "0.33426 fractional expected GOP seats =  2.674  out of  8 seats\n",
      "simpler expected GOP seats =  2.8862  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "plt.show()\n",
    "\n",
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\", state=\"+str(STATE), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",r3(Rsimple) ,r5(Rsmeared) )   \n",
    "print(r5(smearedSeats),\"fractional expected GOP seats = \",r3(nDistricts*smearedSeats),\" out of \",nDistricts,\"seats\")\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5793698b-d681-4b37-a954-29937a12f61c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3108\n"
     ]
    }
   ],
   "source": [
    "#plot tractUse here ...\n",
    "print(nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "74a657df-47e5-456d-9ee4-81690ec2b8f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On a cold re-start, we need to regenerate the countyGeoms and neighborLists\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/1687193767.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m                 \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m                 \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"countyGeoms regenerated, now build the neighborlists\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# start with county neighbor list for efficiency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/geometry/base.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;34m\"\"\"Returns the union of the geometries (Shapely geometry)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgeom_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimpl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'union'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[0;31m# Unary predicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, this, other, *args)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_prepared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mproduct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mproduct\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             err = TopologicalError(\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"On a cold re-start, we need to regenerate the countyGeoms and neighborLists\")\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "countyTractList = [[]] * nCounties\n",
    "for c in range(nCounties):  #nCounties\n",
    "    countyTractList[c] = []\n",
    "    for t in range(nTracts):\n",
    "        if countyNo[t] == c:\n",
    "            countyTractList[c].append(t)\n",
    "            if countyGeom[c] == dummyPoly:\n",
    "                countyGeom[c] = tractGeom[t]\n",
    "            else:\n",
    "                countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "print(\"countyGeoms regenerated, now build the neighborlists\")\n",
    "# start with county neighbor list for efficiency\n",
    "\n",
    "neighborCountyList = [-999]*nCounties\n",
    "for c in range(nCounties):\n",
    "    neighborCountyList[c] = []\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1,nCounties):\n",
    "        if c != cc :  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"done with county neighbors\")\n",
    "\n",
    "neighborList = [\"empty\"]*nTracts\n",
    "for p in range(nTracts):  #this loop takes about 3 minutes for 9000 tracts\n",
    "    neighborList[p] = []\n",
    "for p in range(nTracts):\n",
    "    if p%400 == 0:\n",
    "        print(\"working on census vtd no\",p)\n",
    "    for pp in countyTractList[countyNo[p]] : #for efficiency, look only in home county and ...\n",
    "        if tractGeom[p].intersects(tractGeom[pp]) and p != pp:\n",
    "            neighborList[p].append(pp)\n",
    "    for cc in neighborCountyList[countyNo[p]]:  #... and neighboring counties\n",
    "        for pp in countyTractList[cc]:\n",
    "            if tractGeom[p].intersects(tractGeom[pp]):\n",
    "                neighborList[p].append(pp)\n",
    "print(\"all done finding each county's and each vtd's neighbors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "355d0e12-6742-4c92-a009-ffdc84e9146c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ON A COLD RESTART ONLY, we need to read in each Home District's list of used tracts, and other stats\n",
      "I converted each HDtractList to a list.  Now I'll compute each tract's usage across all Home Districts\n",
      "all done computing tract usages.  Cold re-start complete\n"
     ]
    }
   ],
   "source": [
    "# COLD RESTART WITH NEIGHBOR LIST *******\n",
    "nDistricts = 8  #CO CONGRESSIONAL\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "print(\"ON A COLD RESTART ONLY, we need to read in each Home District's list of used tracts, and other stats\")\n",
    "infilename = \"CO8HDpcL0.36lLmAR1.914Sepunpatched.csv\"\n",
    "df1 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDArea = df1['HDarea']\n",
    "HDvGOP = df1['HDvGOP']\n",
    "HDtractListString = df1['HDtractList']\n",
    "partialTractNo = df1['partialNo']\n",
    "partialTractUse = df1['partialUse']\n",
    "vtdTrump = df1['vtdTrump']\n",
    "vtdBiden = df1['vtdBiden']\n",
    "#tractCPx = df1['centroid x']   #UNCOMMENT THESE IF WE DID NOT PULL WITH TRACT GEOMETRIES\n",
    "#tractCPy = df1['centroid y']\n",
    "#tractCP = [Point(0,0)]*nTracts\n",
    "#for t in range(nTracts):\n",
    "#    tractCP[t] = Point(tractCPx[t],tractCPy[t])\n",
    "neighborListString = df1['neighborList']   #just now implemented.  Uncomment if not implemented\n",
    "\n",
    "HDvGOP = HDvGOP.to_numpy()\n",
    "partialTractUse = partialTractUse.to_numpy()\n",
    "import ast\n",
    "HDtractList = [[]]*nTracts  #convert to list, as these read in as strings\n",
    "neighborList = [[]]*nTracts  #convert to list, as these read in as strings\n",
    "for t in range(nTracts):\n",
    "    if HDtractListString[t] != \"[]\" :  #special handling for empty lists (nonpopd tracts)\n",
    "        HDtractList[t] = ast.literal_eval(HDtractListString[t])\n",
    "    if neighborListString[t] != \"[]\" :  #special handling for empty lists (nonpopd tracts)\n",
    "        neighborList[t] = ast.literal_eval(neighborListString[t])\n",
    "\n",
    "print(\"I converted each HDtractList to a list.  Now I'll compute each tract's usage across all Home Districts\")\n",
    "tractUse = [0.]*nTracts\n",
    "for tt in range(nTracts):\n",
    "    for t in HDtractList[tt]:\n",
    "        tractUse[t] += tractPop[tt]/avgDistrictPop\n",
    "    tractUse[partialTractNo[tt]] -= (1. - partialTractUse[tt])*tractPop[tt]/avgDistrictPop\n",
    "print(\"all done computing tract usages.  Cold re-start complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2fd85a59-2395-40db-829f-65b36501944a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and plot the tract usage distro\n",
      "1.0 0.21141 = avg,std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"and plot the tract usage distro\")\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/np.sum(tractPop)\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 * tractPop[t]/np.sum(tractPop)\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "print(r3(avgTractUse),r5(sdTractUse),\"= avg,std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cc20d967-db76-44fd-a19e-51d3e52f71b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are 0.7 1.6\n",
      "We do not plot tracts with usage between 0.9 and 1.1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAACqA0lEQVR4nOy9eZwjV3ku/BztUrfU+75O93TPPj1Lz77YDDaMxzY2YMBAgtnChUAugXtvli83ySULkHC/fCG55IJDSAhgDIkxGPCK19lnPOPZ9+npfXrf1drrfH+8OqqSVCWV1FJ3T1vP76dft6RS1VGp6jnveZfnZZxz5JBDDjnksHRhWOgB5JBDDjnkkF3kiD6HHHLIYYkjR/Q55JBDDkscOaLPIYcccljiyBF9DjnkkMMSh2mhB6CG0tJS3tjYuNDDyCGHHHK4Y3Dq1KkRznmZ2nuLkugbGxvx5ptvLvQwcsghhxzuGDDGurTey7lucsghhxyWOHJEn0MOOeSwxJEj+hxyyCGHJY4c0eeQQw45LHHkiD6HHHLIYYkjR/Q55JBDDkscuomeMWZkjL3FGPtV+PkHGGMXGWMSY6w9wef2M8auMsZuMMb+KBODziGHHHLIQT9SyaP/IoDLAFzh5xcAvA/Ad7Q+wBgzAvgWgHsB9AI4yRh7hnN+Kb3h6sOtETd+/lYfchLMOeSQw50Cm8WI397eAKfNnPF96yJ6xlgtgPsB/DWALwMA5/xy+L1EH90K4AbnvCO87ZMAHgKQNaI/cuQI/uKNcVyZABKObN6RyqSzuEa+qLFQp2rR2BB6BzKPJyp3+aYFzoFgiOO/vrMl4/vWa9H/PYA/AOBMcf81AHoUz3sBbFPbkDH2GQCfAYD6+voUDyPjD//wD3Fj2cOQvG4M/cefp72f+QRjDF/4whdgt9thNpthMpmSTaAZwXvf8Q6sr67OzM4MBuDyZeBSVhdr8fjUp4B5OFex+IO//Et84//7/1L+3M6dO3HkyJGk223duhUnTpxIZ2hx+NGPfoTVq1dnZF/JUFFRgaqqqnk51qKCJEU/F9dkCtfmyj99Dm5fMIODkpHUR88YewDAEOf8VBr7V/uWqiYI5/xxznk757y9rExVrkEXDIY7K75sNBrxpS99CcXFxXA4HDCbzfNC8kCGjVJJmn+SB4CZmfk/JgC/35/W5+b7+nzsscfmjeRzUIBzeiwS6LHodwF4D2PsAAAbABdj7Iec89/S8dleAHWK57UA+lMfpn7MF0nOFWvWrMG+ffsQCoXgcrmSfyALkDJ9IdbVAT09ybfLJBZoYj9/8WJan9N7fWbqOr777rszsh+9uNMMraxD3GMLzEtJfxXO+R9zzms5540AHgXwik6SB4CTAFoYY8sYY5bw559Je7Q6cCdcaMXFxXj/+9+PkpISlJeXL/RwMoetW4EHHgDy8ubvmC+8sCCW0+joaFqfm09DxGKxYGaeVzy5BIgYcL7gJA/MIY+eMfZexlgvgB0Afs0YeyH8ejVj7FkA4JwHAXwBwAugjJ2fcs7TM4V0wmg0ZnP3c0ZtbS0eeeSRRTEhZdyilyTAbAbuvhvYv58s/GxjbAx4+mlgdjb7x1LAZFqUwq8RrFq1CsePH8fu3bsXeig5LILJLyW24Zy/xjl/IPz/02FL38o5r+Ccvzv8ej/n/IDiM89yzls5582c87/O7PDjsZiJ3mq14qMf/SiqMxUAnSOyZn1ZLIDdDrS3A6tXAy4XcN990e9zDty+nRnXy9gY8PLLc99PCqirrZ3X46WKhXJhjoyMwOPxLMixFzUW2Ge/uM2SJYZNmzbBbrcv9DAi4GYzUFycfMOZGSDN4CNWrKAHAGzZAjz1FNCvCNNIElBTk96+lRgcBG7dApYtm/u+dCDbK7K5TMLHjh2D3W6HFJsJMg8IBoMIBAKL6jpfcCwCP/3C+w8yjMUcjL3rrrsWeghRkATRJ3tYLJk5YH4+MDwc/drt29E3AOdk5VssqR/35ZfnzYXzl//zf2b1WpsL0V++fBkDAwMZHE1qWMz34NsVS47oF7Pr5vbt2ws9hCjMu8VnsQA7dkS/FgzSw2wG+vqAEyeAI0eAN94AurpSd+088QTQ25u5MWtgdXPzooizqOETn/gEvva1ry30MHJYRFicV+ocsJij/ovN0pl3onc4ALVVzcmTwMGDlJqp/P26umgSEDCbgUQTeVERZfy88gowMpLa2NK4biwprDiqq6uRn5+ve/u5Xiujo6PznnEjsJjvwbcrlpyPfrFeZB/4wAdQV1cHzvmiIfx5J/pQiKz1VDA1RS4fhwO4eBFYswYoLIyvPOScVgOBAD0fHga+9KXkKwLOyb8/MgI0NtKxdGBsehoNdXW4cu1a3Hvr1q3D5OQkenp6UF1djWAwiP7+fmzcuHHers/z589jz549eOKJJ7Bq1aqsH6+1tRUATVCpTIA5zA9yRD8PWLlyJV5//XW8/vrrKC4uxgc/+MFFseyfd6K/fj317JgrV6Kfj4wA588n/9zAAHDzJtCSRDeEMVoJjI7S5HD7NlBaGj2ZqOCXzz+vSvK7du3C4cOHI8/7+voUQxrAypUrk489g0h0neXl5UUMD6XxEQwG4fV6UzqO3W5fNAZMWuAccLvl54zJqzyjkbLI7mDkiH4eUFNTg7GxMQDA2NgYjhw5gl27di34jTHvRD8+Pr/H+/d/B/7sz8jlkwjj45RVJDKLZmbo5i4rI8K3WOQaAQDweFBaWIjHHnsML774Ivx+PxobG5GXl4c33nhD9RBNTU3o7OzE+vXrsXfv3qj3rFYrfD5f5C+AyN+5YnR0NKooT5C6wWBAaWmp6kQwMTGBzs5O1f2Vl5fDZrNlZGyLBm43/fZa3MEYZXQBdF34fET8YvWoBbXCQc6Bjg75f/GX83i9nAxiyRH9QqSUJYLL5YrLgDh37hyqq6vR1NQEgCYnn8+HQCAAxhhsNtu8FOTM+7mamJj7PlJZCXm9wDe+Afw//4/2NpOTZP3HIhSi18V7NhtZ/kYjMDyM+3fuxP0f/CB+/OMf4yMf+UjCStl169ahq6sLkiTB7Xbj0KFDUe9v2bIFJ0+ejFoNbNmyRf/3TIDR0dGU6zYMBgPMislRTA7BYBBOpxNOZ6rahosUoRCRdTCYPEYj7hWDgf4Xj2SI3UaSkk8QWcCSI/qFtpKVKCoqwqZNm3Du3DnVcYnVx5NPPolxhbVrNpvxvve9D8XFxVn9PvNO9Hq/S3U1EWqsbo7DQTdkeTkwNKRvX+PjtB+tKt2pKX2BWK+X3DoAYLVGUj/vvfde2Gw2eL1e5OXlYXZ2FlVVVRGffEFBAQwGA86H3U1KS91gMMBoNKK0tBQ7duyAw+HA1q1bYTAYMrYyTef6cblcWLNmTdzrgQUgKE1wDojCLPHXaqVrJJExIFZuwoIOhZIfSwS1rVb5WMkMDpdLfd/zXMEtsOSIfjH4vgG6wTZt2oTz58+r3mzPP/88rFYrAoFAHOEGAgH85Cc/wSc/+cmsLpN1k0kmSGd8PHkgtqyMfPA/+AGwcWN8MVd5eXSxlV785jfAJz4R//rkZHqrjLo6GguAkpISfPCDH8T+/fuxbt061NfXIz8/H319faiurobRaERzczMAoKysDAMDAzAajTCbzaiurkZDQwOee+45AMDy5cvR0dEBSZLwvve9DwcOHIj7jcS1JP4KH3sgEMCLL76Y+ndJAeZkLrD5hPCph0Kyy83tpippm42C6nY7WeszM7L/3W5PvXBJxCsYky30YBI5YYNBnehTjH1kCjmizwKqqqrQ1NSkackLJPPD/uAHP0BtbS3WrFmD+vp6SJIUd6PPBVk9V6FQdCrk7dvk29RCcTHws5/JN+1bbwEf/3i0VZ/uCuTSJbKkHI7o1ycnk9+walCce8YYvv/978dtUqdYQYjaDofDgcnJSYRCIeTl5aGjowP5+fmRCcHv90d6MUiShK6uLt1D0urhsBjuh6wgFKLrQa1i2+vVJtR0RcYMhmiDJ13jZ4Fcy0uO6BdabErccNevX58zGQcCAdy6dQu3bt2KCtQtW7YMjzzyCNzKLIE0kHESCAaJUJ96ioqf1q4lv/bQENDcTJawxyPnuBuN5KYJhYAzZ+Jv2kzeFCdPAnv20A0qJqB0f58Ub3JxHXR1deGuu+6C2+3GjRs3AJCVvGnTJgwNDaFDBOmQelLB7Ows7rvvPnDOcfToUUxOTsJoNKK8vBxvvfUW2tralh7pc04WuvCXC/JPhHR+c84Bkyk6Kydd5Ig+M1hIoq+pqUFlZaVmxsJcIEh+165d+P3f/30wxtDZ2YmOjg5YLBYwxmC1WmG1WjEcKzOgAd0Tkc9HLo78fLrgg0H6K/DWW8CbbxKBK2+GN9+kvxYL8Hd/JwehPvYx+n96Gvi3f9M+7s9+Btx7L/nRAblgShCWGL/Pl/wGvnoVaGigZX1lpRyISwbG6HgGA/3PWPR31wEWswIYGBiAy+XC+vXrUVBQgJmZmcjzs2fPJv1dLBYLCgoKItuFQiEEg0GMjY1BkiQUFBRgenoaX/va19Dd3Y3u7m64XK6IC2nRIRCQ3RyMUWWzsthLnI/GRlqVzczQtSOyoQC6LozG5K6RQED+jNpkKix3ZX2GCKDm58+90Y2emEAWsOSIfiHSK/Py8lBWVoaKigrcEmlYWUBJSQk+97nPRQpSli9fjry8PJSXl8NgMIAxBrfbnZToDxw4AKPRiMrKSn0H7uwEurvJhz41RcRaWkrE6feTtdzdrf15vx/Ytg0Q2Sb//u/6jjszQz75lSvp5g8EyH8fiw99KHHFLEA5/C0tlC7p99NkNTmZfAyNjXMWSlNWqIZCIfT29mLfvn1wuVwwm82RxjOhUAiPPfYY/H4/Wlpa8OCDD0b9lvn5+fD7/XjllVfwwgsvJBhyI772ta9heno68trPf/5zfOITn0CxHhG7+UZnZ7wGkhouXJD/Lygg4jUaZeJONEH6fLTdxIRsOOhFcXHqKwG3W87FN5kWrEGOwJIj+tA8z5h79+7FrVu34Ha7s0ryAPDII49EpbYxxuJS5xyxfmgVbNy4Mb2Vj/JmHBmRXTCrVycmeiD9bIPjx6kitqEBOHWKbrpwTUIETz0FfPCDdENJkrYk7JUrwM6dVBylF+mqdioggrScc3i93kg2jslkwvLly7Fs2TLYbDZYrVaYzWYEg0GsXLkS5eXleOqppyL7WblyJTweD6anp7Fp0yb09/fj+9//PjZt2gSAYgEGgwHve9/7MBETZJ6cnMSpU6dw7733zvn7LApMTtKjulom0URG3tSUnDGTKrT2azLRKlO539lZmpB8PrLeW1tpnFr7UCpb8uw17NF9tzPGjADeBNDHOX+AMVYM4CcAGgF0Avgg5zyuIoYx1glgGkAIQJBz3j73YWvD4/EABdk8gox9+/bhQtjKmI+0zmSCbStWrIDRaMTQ0BDOnj2buQMn+256vvuGDcDp0+kdf2aGyB6QrWsl2QeDJGYm4HAAH/hAvGumo4OIPhGEcqZwJ1it6Y05jBMnTuDIkSOYUliR999/P0KhEKxWKxwOB8rLyyOywm1tbeCcw2Qyqa5OJUmK1GWUlJSgvb0dJ0+ejMrceuihh1THcvDgQRw7dgyPPPLIvMgiZB0lJfot5Uzfn34/XWexk4ckRRs1XV2UoaUru2sRED2AL4K6RIkGp38E4GXO+dcZY38Ufv6HGp99B+c8RZWp9FBTU4NOhxMGuwUoL8fU1BTsdntUnvqdimRNnru7u7F+/fo4q76iogKNjY2Rmztlhc9NmyiwOj1NqpKxYAzYvh04dkx7H4ODwO7dsvsmXdy6Re4UsQxXw+wsWf/r18e/Nz2t3epQkoCjR+n/lhayFoeH6WZOgxh7enpw8eJF2Gy2KKIfHBxEdXU1CgsLsXz5cqxYsQJFRUWw2+2RADtjLEL0GzduhMViweDgYBShz87O4qGHHkJxcbFuDSW1dN4FR7ruVsbIUnc6M2OMZAM+X0ZWhXOFLqJnjNUCuB/AXwP4cvjlhwDcHf7/+wBegzbRzxuuXLmC6Za1MIT8GBoawrJly+ByubJC9PNZQFJaWorS0tKE23g8Hni9XmzatAlNTU24cOECWltbsXLlShi8XiIslyv1i164i2w2Wopevx59czJGVsvq1ZTKqIZQiHzthw/PPS+/s5Mmn0QTy40btIqIJbWREcoIWrUqOjBmNgOXL8vPr1+nB0AWvttN7qPbt+n4Fgtwzz0JA7O3bt1CMBjE+vXr0dXVhZUrV0Z85BaLBeXl5Vi/fj2amppgsVjAOYfD4cDs7Gwk+O5yuWA0GiOkryTzFStW4Pbt2zh16lSU9Z+oIXhVVRUaGhq0z1ssAoF4V1ksrlyJjnfEXhsimF1UJFehKh8A+donJug3YQyor08eWBWuw6kpmrwNBvo7XwkZs7N0byRzCS0CWRa9Z+TvAfwBAGXtcwXn/DYAcM5vM8a0ulxzAC8yxjiA73DOH1fbiDH2GQCfAbRzgvVA+OiDYRK+desWzGYzWlpa4PP50N/fj2AwiLa2NuTn52NqagperxfXxU2dAuYz8FtQUJC0a09JSQl8Pl9kUli+fHn0BqEQ3ZDiphBQpqdxTktSkd2i9CHm5ZHlLmIRJhMtTa9coeBsYyO9duWKuhXT10fL7VQlhGNhNBIxWyza1pLXS9lAGzdGk70QHBsbo+9iMJBr6Pp17TiC309NyAUKCykw/dZb1DVLA8Jy/uAHPwiArpfvf//7KCgoQEtLC/Lz81FZWRkJrjPGYDKZ4HK54Ha7EQwGce+998Lj8WAkfM6EOqTP58P169cRDAbjrkO169JisWDdunW47777UleXTBb3qqzU1wPAbE4cqxkclP9Ptd+wsiBKDfNl0asdx+2mSU5P8D9LSEr0jLEHAAxxzk8xxu5O4xi7OOf94YngJcbYFc553Po/PAE8DgDt7e1pM6haMDYQCKCyshJDQ0PYtm0bjEYjBgYGcPbsWdjtdgSDQezevRvBYBDnzp2D0WiE2+2GJEkwmUxobGyM5D0LrF69GtdU1AuzgaqqKjz22GOa7zscDtTX18PpdCZ3y0gSuS8SQRQ2Wa3y/0LjAyCy9HqjfeC3b9N+ly0jt0cgQIQuAlYzM9E59HPBunWkX58MFy9S31ol0XNOZPPEE0TU73hH6mJrLS3Au96VdLPKykr4/f5I4VN9fT127doVId277rorknETi7ywe6mgoCDyt66uDrdu3cL+/fvRr6gQXrVqFQKBADjneOaZZ7Bt27bI5wDAZrOhubk5e5WtTiedZ5FOq4VUDKN0jahsfEelkmUs9IxTEP0CQo9FvwvAexhjBwDYALgYYz8EMMgYqwpb81UAVMVHOOf94b9DjLGnAWwFoC7xlwFoZd0cDBPD1atXo14XjYyF0JTRaERNTQ3Wrl2LqakpmM1mXLlyBbt370Z/fz86OjpQX1+PoqKiiJWVTWzduhVf/OIXEzataG1tTS6VkOqN4/NR7rno93roEBE1Y9oTxcwMWWV+P6VFxkoeGI1ECskmmmRI5kpQYnCQbjJhaZlMwJNP0v8XL1JWz3/5L/Rcb9WkTp/rypUr0djYiFdeeQV9fX0wm8245557ACAqLTYKsW4PAAgGkW+xoGNoCDabDdXV1airq4PH44Hf74ff70cwXOF7/PhxNDY2puaeSQS9lrDTCbS1AekmAWQ7/TBbFn3sfheR1pYSSYmec/7HAP4YAMIW/X/nnP8WY+wbAB4D8PXw31/EfpYxlgfAwDmfDv//LgB/kbHRZwGhUChSZKLEoUOHUFpaip07d2J8fDwtV0+q+MM//EM8+OCD8Hg8CIVCqpIJlZWV2dHDCQQoTayhgcjnlVf0LT29XuDaNfKRxyIUAjZvpglEuUxPBZwnllKIxfPPkz++uTma8AU+/GEi//5+msiWLyciT3TDdncDzz5L/marlYK269apbmqz2XDvvffi+9//Pvx+P4xGIwoKCiLplhFIkmw5ci5XeXIOHgqho7cXff39uPvuuyOTQ3d3N06dOoUf/vCHePTRR1FWVgbGGHp7ezNH9KmguDhxUF7N2MjLo+8dm0fvdtPvInoIx/4eYl9eL20r0mqnp+WaCs4pJmUwyLo44nUtOQOrNd5VNTISb9WL/0VCgFg1jo6qB/snJmhcwpiIPRdZdgPPJWrxdQA/ZYx9CkA3gA8AAGOsGsB3OecHAFQAeDocQDIBeIJz/vzchpwY2Szz3rBhA86dO5e1/cfiV7/6Fb74xS+CMYaRkRFwziO5+o2NjRgfH9cfz0j1QmKMGnf8wz+QD7aiIiLkhZERdXeH2Uy+7ESl4jduEDGqEX11dXLRMsaA2tp4ZctEuHxZDrR+7GPR7x06RAQlAn+3bhE5NDXR6yaTPLEIshkfp0d9PfmmjUZNogdI5mDDhg0oKChAVVUVLl++jPLycnKzhUL0EGQmiCAQgBQM4npvLwqdTpQ7HGhavx4GQUIGA2prazE9PY0HH3wQp06dQnt7O7761a+Cc47JyUk4nc75lz0Q6pFqmT1q16DRKAe9lRgfp5hOMkxPkwsuFpWVdD2uWJFaNWtpafpSByZTatdlLHhJ+p9NgpSInnP+Gii7BpzzUQDvVNmmH8CB8P8dANrmOshUkK189l27dukqT88Umpqa8J3vfCficy8vL4ckSbBarZEbWFhwupAq0VutlIvOGPnklcReUaFO9Iwlt7Y5jy9YstvJ6h4a0uc+SSergnPKwomtKD1yBHjssWhSmZoi7R0Bl4vOR1ubLIJWWioHIIeH6TuVaN+o7e1y+UhEaz4Ukgt5OKdjmM2RVYshFMKK8vLoxhRud2Q7g9GIxsZGeDwe1NTUID8/H1VVVZicnITH44HP54PZbIbT6UxfGiTV62Z8XFvPJZV9pXOf1dfTyg2gMfh8c5csSAWLILtGC0uuMjYbMBqNsNvtWLduHXp7e+OqDjONqqoq/OhHP4o0JhEwGAwoLCyMGlfWYDLJxUnnz8dbV1VVsj67gCQRKSbyoUsSuYTa2siCF5b0K6/Q3927E7cK3LZNXyBWTB6nTtHzwkKy6tXcTy+9RC4HrdJ48fqrrxKZrF8fnT3S2SlnFKUCzulcTU7KJG+xyKQPkAtNkD/nNNGIFEWXC3l2OzZv3oxbt27B5XJFdHCihz+FoqKihe/VkKlgrMEgrwiVK8OqqvSrXzOBhT6/CZAjeh0IhUL4zW9+A4D0RioqKqJ0RDKNz372s3Ekv6CIXf4bjUREIqPG7Sayun5dn44I52QxC8teudzt66MG4IwRmQp/5/nztAz3euODfmIVUFlJk4fFQi6iw4dJD2X9ehrn66+rj6e/n+IGW7ZopwnabBSvOHKE/lZUyPUFAFmyYhIxGvU1GZ+aoockyf7bUIjGKoTjjMZo8rLbaTubjdwWJhNYYWHU9RKbhjunGE6qVmqi2pJAQBbFSwa1VYEwbCQpuuZBYGaGfhuB8fHUxl9YmJ5sNUC/9yJWB11yRJ/t3PZskzwAPP744/D7/fiv//W/ZmaHnKdXnSdJRMKxapzCxVJZSW6LV14hv7kei8ZoJEGqyUnat8tFlre4cW/dkvP0BSwWYOtW+uzrr9Nxa2qI9EpKiJybm+m92M/OzBA5m0z0mZoaIsnbt6N9wxcv0uNTn6LJRtzwFgtNNj099LrdTrGLmOwtdHeTawiQ6xGSnY/iYnr09MiWqdlMv5XTKQeehaUPEJl4vXJDjYkJSm1MQDJzsuT1VtGG4woJNY9mZsjqVt4/QmYi1uWnlm9/4kRi/7n4DQXuvpuO5XAkD7ADdF3q7VwWC7+fakoWKZYc0WcbKRebpIHy8nJ8/OMfz9wOhaWYKoJBsrzVbi7hjy8qInfLyy/TjRzT+FoVyh6tU1NEUkqyj4XfHy2doNbjtauL8tu1KnODQSL5Eyfo+dat6tv9y78A73sfrQyCQXIzTU/LRCSyONTGqIRY2djtRNRqJDM7SwSvPL+CkLq6aGIxm6kQzeeTM1Q4l905CyR7G4VQiL7DjRupKUMajUSserOo0rG2+/po8mloiLb232ZYckSfbYt+eHgYFoslkrecaXzjG9/Ahz70IVjnKKYVhXQtOotFTofUQihERCjcMC+9lHy/ijgDALJKrVZqDHLsWHrNkwMB7apIASVBDw5qV9b+7GfAu9+tbt0NDlJGjt4VksdDVrGa+0SSaMnPGFnpymwVv59IU/j9hXSFuO4yLb8h2u2Ja0WRw4/+ftkFory/hoZo3H4/TTzd3TRhu93a50cElYNB+rxWXEOpGy8+l+qq1O+XVxhNTYtjUlwgLDmizzZGRkawadMm9Oop+U4Rf/AHf4CPxab/LTSykaOvNhkPDtJj82Y5gJoKqqvjXUwApT0WFNBNLqx5gEggEVkWFNAEpEYuqfpifT6aVJSfE7r+wicPkIshEIi2cDkn91hBAe3DaKTfxGjMbKPpmRn11ECLRRZ6SwTRzOXCBfqejY008U5M0CTFOf3f1UXHKS2lx+Sk7HtXEvHFi3NLVQSi0y5FFtXGjXKbS+FiExA5+4Cclw9EG0pqrwFyMWCsjk/sQ2j/ANENdBgDUqgDTBU5ok8DxcXFGSf6mpoafO5zn8voPiMQF1Kqqx2/Xz1HORap7jfR9v39RHipklhTk7oy5s2bdHMPDkYTSV2dugtI4Kc/BT76UbI6hQUaCpHLp7+fViAGA/n+TSaqMfjBD+iGf897iMRE1yM1xEpIGI10vhXtBNHQIPvhxaqntpYmCb1NY/QgENDX+CMZRIBekigGUlREv/XRo/Jvvnw5vS62GRmhv4yR/76+nia1TKyYldeZSAc+dozE9U6fpiB9SYl8XYyMyEH+sjJ1pVaAYjGxq9zNm/UVAa5dG58ZpNU/IYNYckRvsVioSMRggM1mA2MsrpWbGjjnkCQp4vpRE4pijKG4uBjDw8OR1EbOueZn9ECIVD3xxBORphOALIjFOYfZbJ574YtaEYvauVC+ZjCQj7Oqij4rlvexSPWmTHSebt8mP7/WTaYFLet8dpayb4TscF4eEfzFi2StavmHnU6aJGK/m6i+FAHF9nYiDKW4Gec0QTgcRGpqv53y5uZcrgKNVSj1eOTvVlmpnj4417S+69eTK0Umg5DMUKK1Va4REJiYUI+jcE4TqCiYy4QCpdp1Njsr90U4dw545zsz49LJVj1LhrDkiN7v90OSJEiSBO9cL14VDAwMRBo/xOLhhx+O0r9REr9yglGTnP2d3/kd1X0aDAb88pe/1BS/0o1Uc7wF/uAP5P+ffx4Ip5lGIZMWPUCriFQ1cZLtU5lhs2cPxRQGB7WJvq0tOfkxRkTR2Kg+ltlZwO8Hr6gAYgyOSDBVkogMBwfllZdyPyaTXG0qGqKoQewLUJcM0EIwOHeS18LYWPx49V4rmdDMt9uTX0Ovvkq/hdlM1na60Pu9ckSfGSx4UYgCWmNJZYzFxcUJBc3mFUo/o/Jvqhdvspt4epqWznqJ3mCIzmlPhvx88tlaLFSAxRhZo6JSFaBVjFprSPFdbTYqsool+djNtazF4mI63ugoka2a1c8YrUQqK2nSEBLTAJGYCD57PLQvQdglJfqInnP6jtkKUqqNQe+1kokx6VkFiwpa0VN2iWLJEf1CIT8/PyuTzMjICN54442EzSTmDadPA//4j2T9SBLw6KOUUnflChUQjY3Rjbxjh3b1akWFPl/m8uX6/MY7d8oZGevXU+BPVC4rm0kLlJWRDILTSYQ4MEABQsbIN7xlC31eq8Ly+nWagEZGiKDPnSP/79q1RBZjY9GWYaJGLwYDvS+2m5yUM1iCQfr+ghgdDnqoIR1tllCI3HLZrPIWE7Yy5XI+LfpUK8fnw0jMWfSZwXw2A1Fiy5YtGM5EQCsGNptt8Vj0gYD8AIB/+zf17RLdMHpvPj1+/3XrqBjKZiNCUWZp5OWRtX7jRrS2zvAwTQjnzhHZCS0UkRVy8iQ9v3kTeO97iaSUuj7KSeqpp4Bf/pImCLOZJo6JCYoF/P3fg8e6YmLh8ZAVbrHQ983Lk1cmolK2qkreXjSC0VJATAWSpG/CTXaMvj55RRELrzdaH95k0p8WuhBVpsrvmuh3W8SaNlpYckQ/X9izZw9KS0sjQdpgMJixdoUWiwUrVqzAqlWrcN9998V3iroTMBciamyUCTfZMQAilNhUPLebtOZ37ZK7SgGUwy8USBMJXkkSEfkjj2g3JqmvB/btowlF4OpV4F//FeAcEmNgADQpw2qlScrjkdvhiWBvWMsmyrJVs3JFlotApkkomZWrnPiTobJSf4P4TFjXqU4WMzM0WYtK5Koq+l30rJiEzlOygrGcRZ8ZzJePPj8/PysWPAD8yZ/8Cfbt25eVfc8Jem+cRBkzen6f/n4ivWQ32Llz5CZRc9EIKFcQO3aQXEIq7gqtwrWGBuCP/zj+xl2xAvibvwHMZoT8fni9XtjtdvXuTuJ8ms2UnWMykS9e2VdVqwhMmYOtbPkofM4GgywtIJ6Lmojubv3unmTElK37bb76vioxPEzBWYCC8SI7yGajbKiiIorvqK1e3nqLMrCSEf0CxRDflkTPGMO6devgcrlw4sQJ+BdBl3aBwsJCbN++fX4O1tMTba3G3tSxgda5Zv7ohd9PN01shyo1hJtta0I5fr9ftvydTiK/8nJ1PfRk6O8nbZ2tW2UCNRqJDMKl9qIHrCRJ8Hg82j1/laRmMsm6NmL84pp2OOT3xAQklEKV5DMzQ64sgFYHsfEGrzdzmTaZUqSMRSZcN3PZhzIby+slA0HUzuzZk/5+F6g6d8kRfSwaGxsjDZjdbjesVivOnj0baSDS1taGs+m2P8sCJiYm8OKLL+Lhhx/O/sGGhlITYkomMZBJ9PbGF07V1FChk98vV4kmalRit0e/L278nTuJ8K1WssBEEPf2bfLNC1RUaPe4DQSAH/+YHt/8JpF7WVkUSYtai6RyGYzJn/P7ZWtcWIeFhRQH0IIk0apA6UIRq81MSyXEYvNm9UK1xYBUree5WtsioK+MzYhYi5jkzGb6fQsLM1OkphO6iZ4xZgTwJoA+zvkDjLFiAD8B0AigE8AHOedxzkzG2H4A3wRgBHWe+noGxq0JUWhUVFSE/KYm9PT0oFOtND6MtBsyxKCqqgoFBQUwGAwwmUy4detW2iqXWWkNeKdBaJTU1ZEvvLubfKZabepikZdH1bJKbXurlZbesZ2ARBB0dlZW1gS0tXCU+Ku/opu3v5+Cm/X1kfRGg8GA2dlZSJIU/5tqBf6sVrmUvrhYv1UaDEbvJ5HFngqhJdtWr/57dbW+KutMYj5UO2M/k0z9Mi+PrrP5Wh2HkQrLfRHAZQBihH8E4GXO+dcZY38Ufv6Hyg+EJ4dvAbgXQC+Ak4yxZzjnGhKDc8fKlSvhzndifGQAQ8pycg3MtXmH3W7H8uXLcf78+ShZhLa2NlxI5DtOAN3tAe9EpBqM6umRSbm6Wv/njMZ47Zvr18mnr4whFBTI1bMA5a2vW0dWakMDNV8RgeWxMbqZ16yhiWfzZjkd0u0mF87Vq1RiHy5Qczgc6lXNQqBM6KuI69BslgO0yYiKc1r1TE+nRmqZDAgGAunrE2UbcyH6bLlYFnMwljFWC+B+AH8N4Mvhlx8CcHf4/++DWgz+YcxHtwK4EW4pCMbYk+HPZY3oL126hOm29uQbAigpKcGVK1fSOo7ZbEZTUxM6OztxRtl2Lozr169j/fr1kXTPK1euIKBzGd0s2qFlG6laLZlw3QQC+rIT1JCKz3VqKl5Kobk5PqVQkLrA9eu0zbveRUvrN9+U33vgAVpVGI3APffI9QTj42SJz87SamF6OkL0CaUrDIZ4YS0BpYqk1j4Yo+PGBlY5p9WIshm2krgMhui0x9hjAvSdRCGXMqVT7E/A76fzoZbjLyZIUdGbny930Io9XixE2mm68TO9DU6UUG4/O6uuk688r7GwWGgFqoQy+8xolO85kU6bbn/aFKHXov97AH8AQFl+WME5vw0AnPPbjLFylc/VAFDmvfUC2KayHRhjnwHwGWBuFm0qmjCSJMGd5omWJAnXEwTxZmdn8ZZiqVpSUgKn04meJIp8Vqs1sxLFiZDqTRQrL5wOhoepolSvC0YJvbrlAsePyw3HhW/05k1yiRQU0PdR8y/PzMgiZgLV1RRoVQavR0Yoy6asTA6KMkaBZL1Qu15tNv2qoYwll7eQpNQt7l//WrsjV7owGLR7DqjhvvvSP1ZhYeImKGpQGn23b5NhELuPoiL1uFBlJWVzJeKT9evl1WlXF+1/nog+KSsyxh4AMMQ5T2dtpjZlq65dOOePc87bOeftZSJjIA2kQvTj4+NYsWJFWscJpbi0Gx0dRXd3N9atW5dwu9WrV6c1njsK6S5fU7XQfD6yNN/xDsquEaQ+NkZFVGo+Y7ud8uJjc+eF4JbTSZapz0c5+g4HuVny8+kYy5bpTw0ULhsV96EQy0v1OlNFOi6MbDTYmc9K1WCQfsu8PHrY7fRbCekIMZmK53Z7/PjUxqtlHNXWJifty5ejC+DmEXquyF0A3sMYOwDABsDFGPshgEHGWFXYmq8CoBaF6AWgXMvUAkiQJjF3pKryaLfbYTAYIkFcvUinAleSJJw/fx55eXmaK4mM3Nh6keqNtNAVgemky924Qdb4wACRcUUF3WxjY0TcmzeTBS9WUYcPkzWnFix7+WX6KxQmu7rI6qyqkq1/0bZO9HtNEZxzTExMYDRczZuXlweXy4U8oXGTLkSbQ4DOSaJiMYBcWq+/nr7rRA2pXm/PPiu7rtraaCLVi4kJkppOBbHd0VIhej0IBBZvHj3n/I8B/DEAMMbuBvDfOee/xRj7BoDHAHw9/PcXKh8/CaCFMbYMQB+ARwF8JCMjzxBOnTqFPXv24KCWNosG0i3MCoVCaG5ujqR3xmJgYACzs7NwaOmapAu95d2xUPpyvV4iSmW6WKyssbBQBSmL/4W6YiBArobdu7WPKZpCiCYVyv2n4rc1m0m7prNTljuYmYlOoXz5Zeom9eKL8nfq7AQ+/WkiazW/tPJ8XLxI8YDaWpoAgkFa7i9bpm+MKgiFQnC73XC73Vi1ahWsVmukAtvtdkOSJDhTEXFjLPp31DNhNjcTsWay70I6hoKIL6T6WbM5dT997HlRI3ot92GyiVNAFAOK8Sm1kCb1DzVVzCW38OsAfsoY+xSAbgAfAADGWDUojfIA5zzIGPsCgBdA6ZXf45xf1NxjBpAOAc93wdSZM2ewcuVKGAwGdHR0RB1/aGgI3d3dWLlyZWYPOjUVnQpnMOjTOnn8ceBXv8rsWCorE0scrFlDBFpaGt3ns7eXSL6hgQhfLUZiMBBJVVUROahN4GvWyH5tv5+kl1eulP3HViulWCYjF2G1nztHY7Va6aYtLEy7WIcxBpfLhUAggGAwiLy8vKhres59CVLBQlSnakHvfS1+AwC4/37gF2r2pwZiz20m01AFxsdlt2CsOmphjf7jpYiUfknO+Wug7BpwzkcBvFNlm34ABxTPnwXw7FwGmQrSIfpLWg2lswTOOS6HSaWurg6lpaWYnJxEZ2cnGGPo6enJPNHHD2LhXDF6ZIoBCnaqFSx1dRHZFxfLQVCB3bsp00YrUC602mNlGi5fBjZtIi2W/fspwCpWI+IRDMqNWIqLaXVz+7a8X2E9zs7KcsJpwGQyoaKiAoFAAGazOeqatlqturO35oxMH2cubotY69piISs6dsX11lvpVToD+oh+junYC4VFNGVnBukQ/fT0NFwuF6bSSfmbI3p6eiKZOGvXrsXly5cxo3cZOBfoJfl9+0gy2GAg617v2IqKaOk/PR2fpZAsDqHHLdHVRRkvXm909Wyy31BMBGoQ7rLJSfUlenMz+fR7eojgtapV3W76jsXF2lo5CSCsdq3sK6PRiFAoNOcakKSYrwlFD65fp4k0GJSb0ijF6jIBPUSvtcopKaGVHGPpubtSaRaTBpYc0acTJK2rq8OgHjfGHI+TDCIgfFPpQ15oeDxyb9UPfxh47rnkF3JdnayjYzRSkOvSJdk6T0b0Fy/K+0iEq1fJN75xIxHA5ctyLruWb1ZrIjAa5fH6/bJWjNUqj7urS/786tWyfIQkxVeier1UJVlbm3qbuQTbM8YQCoVgMBjmTvSi523s8c6fB7797dTTWbOJiQk5S6qyUltRdC7QQ/Rqr5WW0nhEgkVrK/3lnF7XktFQorAwqyvsJUf0qS5rXS4X8vPzk+a3xyIbRM8Yw5YtW7B2Li3NlAgGZQs83ZZuSkxPk2zvt74Vv7/qatkvLkkySYdCZEFbLJQ5cfasvvJvPUQPRItNrVtHgVAtkt+2jXLrtd47coT8umfPyvnTpaU0kYRCcm49Y0Tkws/PufqSPhikiSGRTk2KYIxlrs7CYpHlHpQIBBYXyceivBy4di3z+y0spMpoUeQ0O0syGozRNS3cneL8iyBxVVV0N7QbN6L3WVEhuytDIXpIUrQVHwzqSHZPH0uO6FMl4LVr1+KIHpXEeYDf78eOHTuwf//+zOyQc23Nk3QnqoEB4POfl9UbRa9Tuz1xUY7fL4s4XbpECoAnTmgTyunTRLJ6rCGB8+dJ4kCNvNavJwLbsYOsw/5+2s5mIyL3+UjsTGzr85Gf/fx54KWX6GZtbJQljiWJ3heTzPr16mOanKS0Tj3WdwJLPhQKRT18Ph+cTics6eS7J/vts2VZZso1oVfTPlVMTESTtF4IC15rn3qRRfmbJUf0qSLdythsIFv69qqYy808MEDWyPvfT/93dFAP1kQQrhGA/iaTnvB6SQI4kba9Gi5ckEXQlDh3jsYsSUTsXi+lXs7MAEePRm+7YgUFez0eSoNzuWiFceYMrRqmp8kts2KFbP1JElnIwofLOU0swSAt6ecoYhUIBDA+Pg6/349AIIDr16+jvb0dcykuTHCwzO8TyBzRi0YvY2O0+soU0r0n7oBes29rol+zZs2ikij2+Xzz19w8E1bb2BiR2113kYTwr3+tva3BQH7v3l45hz4ZiV+/TkvnkZHUtHEaG9XL38UN6XSSxa6V4nn1Kj127CBrvK+PJrS2Nvoeq1bRdrOz5KsXy/lY61pY8XP0pQsrXtRWmEwmuFyu1Ir8JIkeHk/i1E8htraYIVaOqUhNvM2x5Ig+FddNcbKmFfMMj8eT2TzpRJOG3hxpPedzcJCCjhs2qFv2lZWUuWM0kiX9xhtEoK2tiX2tokK1pYUmCK2AeV2dLBFrscw9UFdXRxkzDgetVsxmcs0MDdF38HhobMuWkesnL09bb8Zi0W7qrRNGoxF5eXmYmpqC0WjEzMxM6s3oZ2dlN15xMY15fFwem2iwzjn5le+5Jzq1lDEqNIu9HmLHoCV+JlQ6F5FhFYd0jZ+rV8m4WMR4WxN9byar/jKETOnjJ0Wml5tTU7SkPncuft8uF+nMiIpXgFwmZjNw993Aa6/F72/5cgrwnj9PFlxdXbwcbmkpWdenTtGKYt068t/evEn/K7XoBfbupRt6zx5yUYRCNK7bt8nSLy6miSgUIitduIGeDZeC1NUREbrd8veZmpLL22OvP2UBTxjiGk119eZyuWC1WtHZ2QmDwZCaoSJ0+IWr0m6P9kebTPIqiDE6f7F44YX0VEcFMl3tnWmke0+k2XdiPvG2Jnq9NxpjDHa7HUajEZIkRVrExTaTYIyBMRZZUkuSBLPZDJPJFClhj4XQthFdiDKudWO3ExmlW/2rl4wqK8l1E3uziCDo9u0UAFW6VCQp2q1hMhGJNjSQJa0kop4e+g5330350zt2EMGLytfr1+Wl/OwspT4q0ywZo7Ho9fnX16srbO7ZIwdgJYkseq+XJorOTnJPbdsma7RonL90yd5oNMJms2FkZAT9/f0oKyuLtChMd593HOrqyG2op4F8Kkj3vC1Qe8BUsOSIPhUU6pTd3bVrF/r6+qJey1Y1bcZS54RP1mZTL3LKdGbFtWvRbpjmZrKyZ2fJygcoc6WlRa5ctNkoeGq3kwV58iRtPzBA2TOx8gWDgxQA3bePVgixgfQ336T3r16l77dlC52DkRFyVehNc2RM3XIVolotLTSRFBXR2JWt45qa6K/RSBONyu8pjIFAIJDy720wGNDY2AiLxQKfz4fBwUE0xrgNtIyKqN88NttJi+RsNjrPiTK45hsVFTShTkwkTwJIBela9Is9poElSPTt7e04mlcIQ74dm++7L3LRh0IhvPjii1Hb5ufnL9AotfGDH/wA+/fvn7uvXuT4BgLqWRRzyawQflghzOT3001nMlFGy+gouU9u3iQ//NatlEo5OEiWf3Oz7CopKiL/pshvLyoikj15Mr7xg9VK2TpDQ+SCuXiRjqUEY2TtnzhB2TR1deTiGRigVMfdu2UXi1av05oaSgEVsFiAhx6iiUzpY/7Zz4hwduwgv73JRJZmYSGdX4OBiFKFRAOBAGZnZ2GxWFKywBlj8IXPSSgUQkFBAdxuN2w2W2rXjN489JkZ4G/+Rv9+5wNvvilP5JlEukS/mCqINbDkiP727dvw1HoAvyeue1R7e3ukdNxgMMDj8WDr1q2RG03cKEILXPw/L5IEYRw/fhynT59G+1wyCkS6XyKk4sqpqZGtQZ+PSLOmhgj3Bz+Q3S+iibXVSsFJzkm4SZkLf/YsWes7dtBk1NAQrVdTU0NZLnl55CY5e5YmBaORXC9iuS5cMNu20ZicTpp4Ojrk5h+Tk3QTOp1ExFeuyNWsDgftf2qKvofQrAHirfl168jtpGa5DQ8Dzz9PsYJgkAjU6aQJzWKJI/lQKARJkjA7O4tQKISZmZmUlChZuBftxMQEbDYbOOdwu90IBAIoKCiQC3KUHaoEUiUyiwX4679O7TPzhbVrgVdeyew+013lzrMoYjpYckSfCKOx1t8ixd/8zd/gxz/+sf7ArGgMrRTWAuRqPjW4XGRJazVO55zI1mZTryYdHJSbKAgf5dQUXfRHjpBA2JtvkkDY5GR0rrrdTi4ag4ECse94BxUrFRSQBbliBe3n+efJYt6yhY6nVvMwNETjGBggP/7mzTTmM2dosqmpUffLz87KriGXK9qfbzbTSiAQkCe1RLIUfj9NVtXVpIJpscipjA5HZCIU7pqZmRkEg0FwzuHxeGA2m2GxWHRZ5JxzTE1NYXJyEoWFhRgeHobZbEYwGCRXJGP69YiUWOp+fT2YC9Erq2UXoStnHjVP5wfZkCaYb5w6dQo/TaVpgijRDgRk3zzn8gWnpaSoVSwmgszd3UR0q1app49JUrSMcCBAGQgul+yXn5oi4mxtJdLeu5es6u5uuinuuosI8fhxqrR9800iWTGJDA+TFd/dTZ9fs0ZuEr5rF5Fwfj6RW3MznYsLF+Tt9GR6NDZG++9HR2nSaG0lyQc92kNDQzSZnT9PN74ollJY0eLaHB4eRk9PDyYmJjAzMwOfz4dgMKgrL55zjry8PFRUVCA/Px82mw2BQEA2ChazumImJ5NsTExz4Y5f/Yp+f7VevIsAbyuL/k7CrFKRMRlExafyQk2UCcAYkVlsJa5wNcSmnXZ302SxbFm0hrbTSYQq3CEA7behQfZlK+UlRLPuXbsoc8ZoJDeN3U5Wu8hyuXCBSH3PHnkS6+ujfQUCZPEDZJUr2wGazUT8zc00WV29mrgBSF0dvZ8oE0fvzW8w0IQxMEDBWoeDVhSKVZmw2L1eL/r6+mA2m1FSUgKfz4eysjIwxpCXl5fUuvf5fHA4HJiZmYHH44HdbofT6ZwfRcu5IJPknA1d/rkaibt3RyupiliWeBiN8v/KCSFRA/gMISnRM8ZsAN4AYA1v/5+c8z9njLUB+DaAfACdAD7KOY9LVWCMdQKYBhACEOSc58rZkqCwsBCf+tSn9G0sSWRBCpEk4TIIhYi4p6fJh+3zUaDTYKD/h4aIDIW7x2QiP7UILBkM5HMXE4bbTcSoJPqeHrm9nkB3N+1frQH4lSu0Ohgbk6WPQyEiRqWbamCA3p+aUi+wuXqVbirljVlXR9/19Gn63Pr1VMQl/P3K1YvZLAepJYkmGYeDxi3OpSTRfoS7z++nz5nNdF5F6mRzMwWirVZ5grx2jVYZfn8UuTHGIlIGo6OjcDgcCAQCyM/PR1dXFyRJQkFBASwWCwoKClBYWBgXqDUYDLBarbhw4ULE/bNixQqUlJTIq1mHI5pw1FYK1dU00QsJabEiBGTxrvFx4L3vjf9supiZkcclCE+QobLxuvI7i7GYzXJBF+d0zoX4n+hglpdH+5ckOVYhkhKEISRJdG1dvSqTq/gtbTb5ehfHCYUo3iIyjySJrhOvl55v3UppwIyRsqv4bHu7dn692UzfN/Z3SdLjfS7QY9H7AOzjnM8wxswADjHGngPwj6C2gq8zxj4J4H8A+FONfbyDc56COtXbG6FQCLOzs/r6hCotBqGKJ24mkbEi+pgKuWGArM2JiWj/eiBALgyjkT7b16edkldcTD50NQwO0mTT3k6uGIGhIdmnLklypavTGX1z795NhK1lAW7ZIt/cAo2N5HMXRVUGA7lRli+nFcT4uEwsdjttOzxMnxMB3h07aGIR50SkSCZScqyqohVIaysRjdNJKxqTiYLQY2N0DIsl4pppa2vD+vXr8fLLL2PTpk2ora3F2bNn4Xa7YbfbI+SvlSZZVFSEtrY2eL1eVFVVRVI0DUriUgYI1cZvNhPxKn+fbMPrVS9iA6LTbtUQO1mroaIiede0xkb6fWN7JAA0+cV2fQLIiBhSa4kdhloTn0SpqCIjax41cvT0jOUARHTHHH5wACtAlj4AvARqF6hF9DmkgJmZGXi9Xv1Eb7WSVe73q/toxQSghN8vW1hFRfQ3Pz96MoiF0qVTXJw422BqSpuofb5omQK3m26w1laalETa45496q0A7fZ4d4vXK7uGALLOpqbUlQ6bmoADBygQrVx1HD0afcxQiCaJRK4dQQyjo+Ra+sAH5GYoZjMFoquqIkQfCAQihXX33ntvpDl9S0sL3G53hNxtNhskSVJ14RiNxogFr5qeqdeFt5gKfZKNJVM5/J2diRMQ1JBI0VLrM8lcMfN87nU5hhhjRsbYGQBDAF7inB8HcAHAe8KbfABAncbHOYAXGWOnGGOfSXCMzzDG3mSMvTkXFcelUBW4Zs2a1JuDKwne6YzXsikupnxy4RtkjPzrhYVEvHY7PWKhPJ+MUTZNdbW+zAJlqzclxsaiu0idOiW7c5T564cPk36OHtjt8k1XUZE4KNnbSxPNzIwceBY4eJAmDIHYHOmqKqrObWkhK/P2bTqn4tgGAy3Za2tJ8Gz16sgyX81/LmSG8/PzUVFRgcrKykigVSvrSrhvRO68mDhUIdxQ4gHIK8DFUgAFJCc+PcSY7N5P9n5fn/r1lsh3r/VeomPFrkbnAbqCsZzzEIANjLFCAE8zxtYC+CSAf2CM/RmAZwBomXe7OOf9jLFyAC8xxq5wzuNMJM754wAeB4D29va0z8JSIPp3v/vdkbL2tGCxyD01xbJd5FabTDJ5BQKyVc45WdYNDbSt2UxWqtJqZ4x8mwAFMZUWtBouX1a3ytesiZcp3rlT1pMRkCR1eV81V4TQmG9ro+9tt9OxhSvLZCIXjd1OwVwRJF62TJ64Tp+mc3L1quwGOHqU/P0GA42lq4u2a2uj9ycmyF0E0IpkYoJWCRcuAJ/4BBF+eFKTwsHSUCgEm80Gi8WS/eBpR0c0oZ89m53uTHqw0BlxRmNiA6WnR7314/r1tDpUc+toIZFFr/le9s5Pqs3BJxhjrwHYzzn/3wDeBQCMsVYA92t8pj/8d4gx9jSArZBdPm9rfOELX0B3d3ekiIYxhoaGBlRWVqa+M7XqvPz8+Lze/HzynwN04ZeVEXEKi8/rpb9uN1nagvC6uyk3fe9eIq5bt2iF8KEPAT/5ifa4Dh4kn/uJE/Kk4XRGW2jV1eoNGlwu9ZtrYEBOqRSw2+kciODt2Bjp1UgS+YWtVrLEA4HoXqO3bsnHqK+nCfLGDYph7NlDaaIiVVTte12+TPusqKBjiHM7PQ08+STwhS9EVg1GoxEFBQWR31q3UaJUhNR63+2WV1BCKTI2E8tgiA7SzjcSEX2ytMRE7SH1QpJkMTs1HSNAfYyvvAK8851zO7YSWc6wUYOerJsyAIEwydsB3APgbxhj5WHyNgD4n6AMnNjP5gEwcM6nw/+/C8BfZPYr3Jlob2/He9/7XtTV1WVGsVLtAh0fj7+olBakSPMKBMhloua7nJig7UTD7JdeIs2XHTuAp5+m427fTvt56y3yf8/ORvs1jx6lCcLjoRtMGS8wm8kvr6YdtGyZetZNV1f0amL3bvLrC6kFgKzrU6coy4cxqqBVU8kUsNspNVMUdo2OEpkzRhZ7KBSvq9LRQcE9t1uWgnA6ZRfYwADwT/9EweOdOyPn3qC1dFe2K+ScJgijkX4fk0n+LUMhOU9/cpImtdiYidqK0Gxe2BaBc7HoW1vVrxElkk2ckkS/6Z496u9v365eHGi16pf1XqTQM/oqAN9njBlBPv2fcs5/xRj7ImPs8+FtfgbgXwGAMVYN4Luc8wMAKkCuHnGsJzjnGqkamcGd4LopLCzE3/3d36E8LJIlmj3PaewiVVFApILFBmFjb7bJSSLgZcuIHAYGiDiUmuJKkgEoA+EXv5CfC+tIWNC9vXQzBYP0+a4u4NVXKXth795oV8LmzfHWVV4epc5p9XcFZOuusFDOmlHm81sstE1/PxVlJdMjqa+P7zQlvr+QRm5uptXH5CSRfGsr7dfjoQlRZI4oFTovX6YUVJeL9HIeeYReF/LIIr9aWOWiyvn6dXIP+Xx07gMB+TuFQrTKkiT5PLjd0b5fkWKo/P0TZbUsNDIh2aH3/tHaTlyvsdu+4x2pK2UuMh7Sk3VzDsBGlde/CeCbKq/3AzgQ/r8DQNvch6kfi53oW1pa8O1vfzvKPcM5hyRJc/PXKn3vIgdcDUorPhgk0hbblpQQqZWXUyYOQDfY7Ky+ikslwallyzgc8T79EyfkAiqBtrboQis1HDlCk4YkxVvzgJyKt2wZ+csT9Z51ucgiVuZyq0EItQF0DoeGyO3j8ZAeTmxNgRJTU6QLVFNDYwWiJ2ZB8oDsepmYoH1bLDQ2ZU2DUJU0GsmN5XZTYFiSyI0E0ApNeYxU+u9mA1oWvdWa3HWjJxUxG/e+2ZyY5KenySi5cEHfWAyGBalevrPXIypYzES/e/du/PM//7MqoQshtTmNX+mfzctTJ/vpaSJ5n4/cIsqijpER2SoVecNWK5F9Ok2oY6FGNJJEN4nQdV+5kki8oYEex4+ruxtELr3NRquHwkJ5tXD9OvnV167Vlyc+NUXWfH09kWZPT/LPCHdXUxMFXPW6JcQEqoQo0hIQv+HwsEyAfr9c2CMqKYX6qijgCQZp0vJ61dNkFzoYqnVtt7TEE2UsMpF1A9CErrUvtRjAmjXqbSkFLl1SP69aY9m4MXrlOU9YckQ/NjaGYGkQPBiEe2wsSvtGKP8BwPr163H27FlwzrFp0yacTtJZ3mAwwGAwYNWqVXj3u9+N+vr6yD5jj6FsAKF8z26344UXXlDdP2MM+/fvhzldrQxR2SdgtcpEABCpC3+3kC9mLD6rJRQiy/4nP6EKTxHQCwSITAXJGI1ymzmjMfq5cPWI15QXfV1dtH67KDZRloG/851E0F1d5NrJy5NdHcEgPd56i+IEXV1kZdvt8qRVV0cWdmFhcgJRorubJpdt22TXh7KASg0dHfRQav4kQmzMJBSSJ7KhIdlFISovg0H6jHB3KVM5xTm0Wum3FwqcNps8cTocciql1bqw3ZDmEoTUM0np2aaxMXr1mOzz6U6OWt91gSbbJUf0brebslgCAUxOTmpu19fXh4lwpsfMzEzCbQVMJhPWrFmD5cuXpyWeFgwGI52ksgIloSpXDbdvk1sjFd3s/Hwintgl865dZBVZLDQRnD9Pfuvycjq+z6dd/agX69aRHxyIbh2oREFBtBuouVkOlvb00KOxkdwkRqO6/10NXV3xvv7t27XJQUDP9VBVJTcmERC/ychItB86L4/OpdEol/8Lt1Jsf1pRNFdSQq4eUbClvKbr62kCFNIHSlnm+cJCryiAxFa/2nvpVq/GflebLdr9Ns9YckSvF6JsfPfu3Tik1YAijN/+7d+G0+nEO9/5Tvh8vowqZC5btgxmszlqtZFRcE5SBalOMPv3U7qekkwrKuhiFdk5FgsVBJWUyD755cvTH+uKFXRDJOsEZbPRcUVjkfJy9Qmhro7GVV8f3cSkuVmfIiVABHv4ME0+Fov2xKPnmvjoR+PJRJLIyhZpmQKMyUVgPh+RvSAdoWukRDBIBT9a8HplkikrowlnYED/ecgEFgPRJ4Ke+0+4DJVwOuMFApXf1Waj6+fWrcS/URbxtiX68bB1lKxH6+/93u9hx44dABDp7JMOjEYj1qxZE/HBizL3+vr67BbNMAZ87GNUBNTZqf/GzsuLD5y2tkZbu83NFIBU6obcukUiUMKNoyS2hgbtrk7r1pG/M5nF095OKwlhoQuNGzUIYuzuplWIEJq6eZPITilElgxilbJ7N8UQYi29ZJZfcbGcj9/YSJOjyJI5eZL8/C5X9KpLiM15PNETweRktK9fKHxqobY2XqN+epp+49276f/Ll9NroGE20/ewWuU4TjBIxFdaSisLpViaGgoK4oXYYqF3khDuylWraKIUGWTioRYjERDnVRmQd7nIBQjQZHn8ePS5Zgx417viv49odwjIGXE1NfJ3UdPHCWQvvvi2Jfrm5mb09/cn9In/7u/+Lnbu3JkRC95kMmHVqlVRVjvnHMFgMPvVkUYjWcArV1Lxx+XLyT/j95N1LvLh7747Pig6MED9W//jP+TXQiFtvZxEksH9/TS+S5foJmFMzjoREN2olEFmraX4mjXRwd+xMZrsgkHKUHG7KVirl+gFDh0iAlu1iqyzjg56PZlbbNMmGo/oirR8Oa04hDqmCPi1tBBh3r5N269bFz35CX+7202kJpQctUjS5UrciEScy7VriWy7u/UXVbndiVM2BwdpAmhslHvrHjggV0CLieHwYfptEwUp9QRaDQb5u6plyhQUqNdlCNy8KWcsCQi5DC1YLPH7LC2VK8gF6uvliUcLpYukMnYp4dKlS1izZg0uXbqELVu2YHR0FB3ipoVsyadD8kVFRTAajVFZNBaLBcPDw1HSBqFQCBMTE5krmkqEgQG6CdQqUNXAOZG8zQbcf7/8eSXGxuJvjGT7FOmdot+seBiNtK+CArrhxGSxdy/52x0OWkE880z0PgXBlpdHu0CMxviso9isn3Qn2JERWkUYjeTz7u9PTEQ1NfErFY8nvt8tIBOnIMGpKSJLsX+LhSbhyUl6iKCrFgoK9AVgg0E61vT03GMsSvh8MukVF8t9e00msvo5J4s5EQHrRSZcQ7G/YzKXp9pKSiQOKNHdTSurigqakOY5RvK2JfrR0VEsX74cFy9exMjICPbu3Rsh+sbGRuzduxeBNJv+1tfXR2IASszMzKj2n3W73dTvM9PgnAKqoRBln0xNRWfiJILFArzvfUTuWo2k7XY5ACpK7ktKyCpVjsFqJd/2oUP03O+PdxPk51PAOPb8KN1Hdnu8vOvAgNwQxW4nXZJz54hEk8naZqIB+/Q0BWu1JupNm2iSig329/XRjR8KqZ8LSaK/o6O0ndGovq0geYuFiFSZkWMyRbsrxBgZo/1MTc2rVC4AdevYYol2/XBOk7q4FoqK1GWFU0U63zWZK1GSyEWj9NH39FCRXuxqsbeXHjZbtKjfPGDJEb2aBV5cXIyxREsmIIqY/+iP/ihtkgdSz+UfCVuaLpcrc3UAwSAthZUdmAD95DY7S6mNTz6p/n5dHVne4+PkcgkEaKm+ejVZu01NdEFfu0ZjMRqJ9Kam1Jf7MzNklWu5GWw2uamEEnl5sivK4yGSNxj0Bb2SWfQlJURABQW0HH/rLXVf98mT5PqIJXODgb6Tlpb58ePkWlm3jvZjtRLJCVkDgYIC/cSgvH6CwcRuG5GCK0jKZEqsu54tiJ67SsQGvbdvT5zPDiQncj33Vux9n+wa2buXJigl0YdC8cF1JRYgKD3/6jpZhhrRNzc3o6mpCY1qfU/D6FXMvq+99pqqRa4XXq8XXq9XVw9QgZGRkUi655zh89GNo7bs9HqTZ7UIiL6tavB6aUk+NUXHuXmTluCFhXRTdnSQv93ppHz32lq6eW/eVNcacToT+0K3bFHXOokVSAP0W27JJj2vlyzJy5dp8iovV3dVhULqhHr33cmJc2pKdqdxThNULOmdO5deoDQZfL744G+4PmReUV+v3pNYiTNnaLW2dq2sFhqLZESuJ/MsVXdebBe0++4D7rkncaXvfK+isASJPhbbt29HX18fOjo60N3djebmZphMJmzatCkSiF2xYgVKS0sjGvBPPvkk/vZv/zaSmZMKCgoKMDo6iunp6ZSIHgCmtGQL9CIQILKcnNS+mPx+Iqzi4uRE53ZHu2GUEG6A1lY5eNfbS8SmLFIaH6cMGRFokySSKF6/nqQONm6kG3fHjsQBzUT6JOlC3NQ7dlB9QGz1r9sdnS7a0UEkvG2bumiYErG57olw8SL9FolcTdmqv4g957W12TlOIoyP0/W0a5f2Nl4vTXgXLtC5D2fCRWGuevRAvExxqunCgQCtPBI18MlZ9JmD0+XEnj170NXVhf6wf0+SJNy8eROcc5w+fRqHDh1CXV0dBgcHkZ+fj9nZ2UhWjNlsxuc///kIWeupWDUajbDZbPB4PCgtLU05wCo6EKUMSSJyHx2VJYcTYWaGLuhly8gloQXOqUpVTRM+P5+aNFy6FO0/PXs2uULi8DDdtGfPkv/a49FOu0yGueQlixvfZKLMj7a2eBdJ7PkJBMjlUlJCk6UaDAaavFJZod2+rb29kHrIJIqLafUVG8hdKJVGr5fSf9/1ruRjOHQoseDdXBA7GSTSoN+3Lz1t/wWw6Jekj97pdGFmbAgHNfKrlbnzPWFdk0OHDmHLli0YGhpCXZ3cLOuTn/wkHnvsMbS2tqKurg6zs7Ow2+2qLqKioiJwzlGRSiaKApIkoaenB5WVlfo7TEmStpsmGWZnibASiV2FQsCnPw38n/8juw+KiijgFCvbC9ANK4KjenD7tpyimAhaMRaR9ZIOGCP5YBGgPnkyvlGKViC6t5dWJfX1RMRms+z2KC0lshL1BLFdutRQVhbvNhDXqZA0UOaDx243O0vHFbn5IhhrNkfLTYi/FgtNyGJiUwZx9+6l/Xm98VIVAK1A0oGS4IqK5CCxyApijAyHTZu0g/MCxcVyXrpAXl7idFlJonOU6HovKYmOBVRX0wpCLUvGZIpPRTWbaVyJDJDFqEd/p6G1tRVHpqcgpVBqXF5eDpPJBL/fj9bWVpw5cwYrV67Ezp07kZeXh76+PnR3d6O0tBSVlZVoaGhAfX09zGYzAoEAZmZmUF1djWAwiN7eXhQWFqKoqCitwCrnHKOjo5E2cUk2ln3k6WJ6mi7WRMtJt5sycERgNlZ+IBZ1dfqIPhXtlQsXaAWhnFw2bKDA7qZN6r1htVBSQqufw4fpRlYu12/eJPIT41q+PFoVUwllQxLl8X/v9+Sle1ubPv/6pk3xr4n0SeGO8/vp3Mf+3h0dsptJeT4rK9V9zsKI0LpuSktpglNzPxgM6RO90jXZ2Ai8/rr6dmLiXrGCAvxq5L1qVXxQf+3axMf3eCjWk+jajZ0Ejh6lmJMa0avdM889R8kJiZBqm9AMYMm5bi4muAgrKyvR1taGPXv2YOPGjZHXA4EA8vLycPbsWRiNRmzevBlWqxW3bt2Cz+eDJEnIy8tDeXk5SktLIUkSRkZG4PV6EQwG4XQ6MTQ0FHERTUxM4MKFCwgGg2nl4fv9/uT+fc7ppp5r309JohtapH5pPfr7qZvU8uXJfYwXL9JNmgxVVfGl44nQ0xPtwjhzhlwpQ0OyNb17N/n9lXA6ycqqq6P/Kyoo0wWg76OclPr76bwWF9P4rFYqvkoX09NkPSe7udUME5eLPldSQufcbifittnIenU66SGuldjfRY3kjcZoHXs1mEyUPbVvHwWUlZhLVpjJRO4ip1Ofn3pwkLZXI85s+Ln37lUPnqd6rOvXFyaonQBLjuibmppgt8UHysxmMyorK3H27FkcPHgQo4pilfHxcTjDS9jJyUmcOXMGIyMjqKiowPDwcESFsr+/H9PT08jPz0deXh6sVitmZmYwPj4ObwzhTk1N4VKyjjgJMDw8nFieQejEZwJ64wK3bxMxNjcn3m50lDJyklk2CheZLoyOkkWmRHs7TUQmE1lely9TGmRVFZH+rl1E6n19NFHMzpJ74MYNsqK1KjvHxuj7Hjw4t5znzk5ajbz1FhH18uXxLgdAnXiF+yUvjwjabqfnQpLY4yH3i7hOxPYAWeVa5f6JjAizmSaY/Hx6xMYG5uJ2GB8nt0hfX2KrWomJCfqusZk2qUgD64XPpx5f0jpfWseLbfgSC49n3oPeSV03jDEbqMerNbz9f3LO/5wx1gZqH5gPoBPARznncWkjjLH9oAYlRlDnqa9nbvjxuHbtGjylHuTn56Np+3YcO3YMe/fuxZtvvokzimW/yMCxWCwoKSmJFEsdPXoU73rXuyIdn8bHx2GxWGA2m+FwOOB0OjE8PIze3l60tbVpjsPhcGDt2rW63DcGgwHl5eWRZtEp9RPNBGKLkBIhlcmlrIyW63V1ZBmfP083LudEnmYz+bkBWaZY6YeO9W0LgS9B9ozJBCnkggVu31ZfbgtS9HhSc/ekAiUJKc/r6dN0gxcWxvtwA4H4jA+rVV6xOZ1yAZTTKXenAqIlkvPz6RyXlGirMdps6pO71Spb/GrfBZgbmaZbiez10ngfekjOTFIzgpJZ3qtW0SpBC1qf15OfbzbLE7HZTOe4qir6mo79THV19HP/wmrd+ADs45zPMMbMAA4xxp4D8I8A/jvn/HXG2CcB/A8Af6r8YLj94LcA3AugF8BJxtgznPP0Td0kEC6PmZkZ1FosaGhowBsa1sNNhcCXzWbDzp07YTKZ4PF44PP5cCLsm73rrrtQVFSEorCFFAgEsHr1ak1pY4vFglWrVuly29jt9kiMYEHAOVneAwPkJigvJ3eKVvONggJyb+zYkVz69/hxIjVlhsT27dQ6cM0aWfdlLlBLs8s0MjXp3ncfTZRqGVxqxMsYrZ7E8aemZL1/4Ys3maIJyuOhcytcB0JYSxmIjZUwZozcS3rIc66NcVLFxo3kN2eM4ieJAql5eeRyEha1+CuOK4LLSqno9euJ/MX33L5dDmaLc8I5nVPlPjdupN9j/Xp6Lfa7uVzRyQuxLla1VFrjAmrdcGIrEfo2hx8cwAqQpQ8ALwF4ATFED2ArgBvhloJgjD0J4CEAWSd6AJoErwav14sjGu3rXn/9dezcuRM+nw8bN25EdXU1fD4f8vPzwRiL6NpwztHV1YVgMIi2trakYmWFhYUoLCzMvqhZIojqzbIyubORyyUTvehWNT1N/589S26PoiLg/e+nZuFTU+STn5iQLSabjW6O2ArHjg7Kzc9UXni6jVpSQaruilgL0GIh/68gUiV53n03PS8ro5tfmWUi+sUKCBeSaPQiCKahQbYaS0rUUzEFEQn3TqxhoeUmzOTKMh2/+vS0/gyuUEg7S0oLK1cmFhpLdCylyywWyu+6COSZdZmRYcv8FIDlAL7FOT/OGLsA4D0AfgHgAwDUHK41AJSmYS+AbRrH+AyAzwCIdG9KB8lkh9PFkSNH4HK5sH37dkxnoEtPaWlpdvRt0kXs8nLvXlmT5t//nar9Xn1VdjmMj9OjsZGI2+2mFcHzzxNBbd2q7ocdGpLlDkQD8YsXtXvcJsORI9HFVnpaB6ZzjL179fuVY4leZIMog6Z79sjL/bIyuX+vEqKzl5jMRCBWkLRoCF5dHd2hKxksluRiaIC8T2UdxVxWnunkj2fbhZnK/tUmSC0oyX1iglYRC9HsJQxdo+achwBsYIwVAniaMbYWwCcB/ANj7M8APANALYdM7SyqTm+c88cBPA4A7e3taU+B2SJ6AMjLy8PU1JSuHPf+/n4YjUYUFBTEEbrJZIJLrQhpMUEU6QSD5Nb54Q/Vt/P5oithhQ54oobKs7PRHZs2bozX5NGLYFB2IWUrwMV5Yu2SWCjJQ6yYlCuYyko5E6eoiNxbMzM0ac7M0OsiM0a52uNc1n93Omm7WNeNHuhpUG000m/LGAW1ld/t6adTO57yuIsJ73ynLMOtB3v2yFliyQyT2N9kga36lM4853wCwGsA9nPOr3DO38U53wzgxwDUOlr0ItrSrwWQARk6baQqO6AXjY2N+MIXvoDKykpd2585cwadnZ2wqSyjCwoK5jfYOhek0nMVIKIaHqYcd70YHNSXjpkMyqCky5XZKs/ubv2SzKOjlFlTU0N59LFuKkG0NTVE9IxRELW0lMi+vJwmg9paionk5dHDYpEJw2ik58ogdipYCOJRjnHVKn3nM5v3SarnIJWxxPLQArUQFNCTdVMGIMA5n2CM2QHcA+BvGGPlnPMhxpgBwP8EZeDE4iSAFsbYMgB9AB4F8JHMDT8eoVAo41Vg999/P/bs2ZOyL91kMkWJoxkMBuTl5S0ul00iMEZui1gobxCti1+vHDJAwd2KCrnFn9NJBDc+Tq4jvemfhw8TeVZUUIZLayuRrprue6oYHCSNG4cjuc/Y6wV+/GP6v7ycCqiEZpCwau326CwblfPIOcfY5CRKhG6OcOGE031DoRCCwSBmZmYwFHb7VFVVwWQygTEGu92ufc3m58tN19V81MEgHS/23M9lgmhslD8fCtHvvG4dkaIyMF9ZKVf06vXPp4qVKxMHdueKReCXV0IPJ1YB+H7YT28A8FPO+a8YY19kjH0+vM3PAPwrADDGqkFplAc450HG2BdAgVojgO9xztMsq9OHTFj0NpsNeXl5qKysxIMPPoiioqI593NljKGoqAiFoi3ZnQCLRTtQlYzsU7WmBwfJ57x7NxF/ICA3agCI9PU0xOjrk+MI165Ritv+/RQ7mAuEBS1JNMazZ7WrepXnpqSEJp/Y6ydWQE11N1S7MTIygrq6OnDOkZeXB4CSBwKBALxeLzxht5LRaERfX1/Efdnc3Ix8rQlXGZMR6ZZKGQQgtWbyehHrAvN6ZS19ca3dvk1ZQ3qkMdKFyZS8X8ESgp6sm3MANqq8/k1Qfnzs6/0ADiiePwvg2bkNUz/S8dHv3bsXRqMRK1euREFBAfLy8iIuF+FiSbedoNlshtPphMvlymx2zXwsBbVyjkVOu1p+sEA6xVwzM/HiZqI7kdJPnAqEJvvataSgmW4gfft2WQOnq4ssQklSz/JQXiuXLwNf+hLwzW9GV8d6PBSkKyjQPIcGgwE1NTU4cuQIOjs70draitLSUpjNZrjdbng8HjgcDuTl5cHtdsNkMkUsfUBnXwSRrqmm9Z9plJTQxBsblPT7KS2yt5cKzCyW9H8nvUjnu6bCAen46LO4CFhyWjfBFNP2tm/fHknDfPXVVwEA69evR2trK06fPo3a2lp88IMfhMfjiRQ0eZIE5pxOJ1asWIGysjJwzjEzM4PR0dG4yUKrE1VCSBJZQbH6KaKAJhDInF9TS5hJmd6nhqoq4LXXMjMGgLJqtNQtd+4kMj99Wj1lc3iYLNaentQyZwTq6ykrRhk8BkhqGaDiLWXg2eWKD3Qqc7kFZmboUVysWiHc0dGB3t5eFBcXw2AwYGxsDNeuXYPb7UZZWRnGxsYQCoVQVlaGyclJ2O12zMzMwG63IxAIQJIk/XEgEdQVVaiJwHm0xIQyP1/8VX7f2CbY4RVJHKamyM1VV0e/WaqxIeUY9CCdFXoq99UiC8YuKaLnnMPj8UCtYH3lypXgnKOkpAQ3btzA0NAQ9u7dq5prf+7cOZwLC1bV1NTAaDRifHwcX/va1/ChD30I7e3tEdLOz8+PEL/QtfeFy6jHxsYSdraqUSuFT/4licyFRcIYEYtIlTMaZQXDuV5cyuCmEslWE4wR4c1VXx+g0vdEUhK3b5Mfd88esgaVhV6MEVGL3PRUyWP5ciLA2FoAJU6eJF+zsIxFC8OVK8nPfP687G+O/V0YA/7zP8nij8Hw8DCuXLmCQCAAm82G4uJiuFwumM1mWK3WiKie2WyGyWSKtKhUErzuVajPR+dIzyqRseSdnpJ9Xgter6x+mg7RpwKnM7VMKmBuFv0CY0kRvZoY2IYNG5CXl4fDMRbZhg0bcCrRDRzGwYMHo+SOn3zySZw8eRJf/vKXYTAY8K1vfQvf/OY3UV1djcnJyQjJJ4PBYIhqFK4b4kYxGolYAoFoX6q4Waen5+5jnZ6mYFlJCVnCepe7/f1ULJWscjYRVq8m6+/ixcRuoMpKIntJIoLo6aFJZv16slCVhLF6tX7d+4ICciHo0SuKdTOEQsDLL9P/Bw6QpfrOd9Lzz32OfruWFloVKMvgFRBuxIGBAQwPD8PhcKCwsBA1NTURt6LP54tIeBQWFlIiQjgQmxJCoeQ9BDIFPWMbGqLYTCK5gsWORZZKuqSIfnZ2FqtWrcKU0QSrw4H29nb09/dHVCWVOKOmpa4TN2/exOc///nIc6fTCZPJhOLiYgwk6iyjgFhiW3QE5aIg/OKcy2QPyJaiELyaK2w2yrgZG6NHTQ1ZpmVl6k0famvpxgwEKKNjDucXABGt6DPr9RIZWSxkhRUWys1WLBZ6//Bhcs0INUo1Qj90SJZgSIYVK7TliVPByy9Hk+irr5Lks5hAiovpu8UQQ4HLhYKCAjQ0NOB22KddXFwMh8OBYDAYaXIjYDQaF7bCWi/09m3duJEmUJuNfjefj1JREzX60Np3aSm5E0tKZJdiOhZ3Kp+JNYpGRuQ+BQswgS0povd6vbh8+TIq24MIeGfxZjaqJFUgRMgYYygoKNDUwFFCt4Qx50TcQtCKDijLFciDIL99ombQqUKZlihJdLPl55N87fnzcvFIQYHcvtBopBsq1WVxLMxmff70/Hyy/mpr6diJCmCam/UVyKxblxmSB2SSb2sD7r+fzqnSndfaqu4aqq0Fqqtht9tRWVkJzjkkSYLBYEjdOEgG4XbKVrvCdCCqhD0e+j0KC+n3feml1AOpBgOtCt1u4N57KZunoCA1iexUETtGr5eCzVqtObOMJUX0yYKk2YKSsB0OByRJgtvtjnMjmUymSHpcIBBAV1cXWlpaki+1PR7ZFy+CXWrI9PJbzWcrgpAWC7kjbt0iF41og7d7d2bavOm1nkRQc3CQxqRWZet00uujo5Snnwznz5OPXXzXVKEc++rVwMMPE7mLiVO8/973aleoxlxT4+PjUcJ3whefkcI7u50ew8PJm6QsRKGf6KLGOUlxvPhiap/3+8mtNzNDJJ/MIMgEYq/foiJZzXUBsKSIXq9/PNNQEj1jDPn5+RF9e4/Hg4kwCdbU1ESkjg0GQ0SSOCGEep4QURLWvFI3RSxn002PKyykSSLViXJ2ljrqKJFu79dYpGNd+v3qBTYVFeQqSeX68HiowKezM/VxKH/Td74zvmDrvvvot7RadU9oRTHa8nOt61BFtkm8tjZ9qQuAxjc+Drz73bTai71etc7lxATwm99QP9rh4flJTY49l3a7tiLsPGBxRQzmiLQaa2cBSvK22WyoqKhAVVUVAoEAJicn0dvbG9X4JCGmp4kohofJqhHB15EReoyOEsGnQ/JmM1m7vb3yMjaVilYdLqq0ke7NKDKZzGbKGtq5U25MkkpudldXZrRzYv2xBw6Q26CoKDHJL7REQSI88oh2Y/RE8Pnmfs2EQnStpltXMRdkO48+i1hSRJ9NQbN0wRhTtb7cbjf6+vrgT7ZUFsJiAP0dG0uv8zxAS9aCArmJxeRkdPHKzExqVq/VCnzqU9H63qlAaKXHvrZnD1WepgNBQA0NRNZHjtB30shuSYhEKxyTiX4bh0N+2O1yE2/hDjl/XpYN3r1bfxu9+XaReL10fTgcsraO2sNup/Hfey9VHKcC5Xdqbgbuuiv98U5OUnex+USqZL2IUiyXlOvmjhEKC8Pv96OnpwfV4aCbKubijhIqh5JEbpapKbJqE1n/k5NkFUsS8OlPAz/5ibYlLAJmu3enVq5uNNJNevo0ZclUVZHVXVFBGTHLlqX/vW/dIpfLjRs0YVy/LncA2rOHyN/nk33jwjUGEMk1NVHTb85pXwUFtE0wKGc2+f30XMu9pNRtGRmhIqADB9RbCGoh0wHXZLBaU9MVCgaJ+D/0IXKNHDtG187WrXTOXnpJ3rasjB4OB12DVVV0/j0e4MEH5cQCj4eyp/RUVQeD9Jn9++n3MRqpCjkR5ip5oKfJu0BnJwVexb0zNDT/v6kCS4roFwodHR1xPtRYWCwWFBYWqk5Gmtk3Il0ymSyy1ufd7vhy82RWhiRF+xLvvhv45S8TfybV5h/Ll5N0QCBAhCwCY8K/nsoNBZDYmKgwFtkjolBKpLvevk1EqxVwXLeOxpOJrlexyM+nNNHFlNUSC71GUux2wSB9vwMHaBW2bBm99uijFJS/dYsC4WIl090dTbixLszNm2nbZ3WopkhStIZ/IpEyg2HuWTapxrBif29xDjKVAp0CckSfAfT19SWscq2urkZtbW3qec7CEk8XmQjYVVYmDkoaDMAPfqB/f7t3Uzphopvm0iXKiR8b01ch6ffLmT61tbQ6UENfn3YefV9fvIBbXR350u32uWUSXbhA+fRCtVT8Llu3yn1vxSpLq8foYoffT9LDgsACAfp+JSWpGQKhEK0QNmyYey2GEpJE45iLe3cuUtChkDwpLV+uLS+SJeSIfh4wNDSEBi05gUQwGsm3PDycHSVBPfB6aXkcCACvvx6fliZJwMc+RuQcK2lcXU0EICytggL6P5ll5PdTVkV1NfmCY904QlBN4Pp1+f/eXiIXrWC3yUTumVhX05o1dPMpX6+ooI5VFov+Qis1vPUWZXwo9/2e99CKI92G1HcCgkFtGY1kqK2layqV1V2yczZXwydT+vWJJows+fVzRD8PCAaDmJ6ejrh3JiYmMDw8HAnUMsZgNptRq5blYbORy2FkJLPFUKlALLXvuYdcILG+Tp9PJvmdOynTpKSEblSbjVYFzc00/lSCrP39ZFFfTEHZuqAgsf78oUMkRBZL9AMDNO49e+hm6++X2xKKFUNbm+y/TxWx/lmXa1EF6xYdxsbIwHjuOf1Gzle/ShO5JMkpyJxTTMDnU5fWjiXd8+ej34vdzumUdXLEMcRxAP2/qSicEunSAHAre6u4JUX06UoJzwcsFgu8Xi8uXryIWQ13TEFBQST/PgoGAwWz8vOJxIRlMt91A2438OEP0xL0xg0i8mXLgH/5F8p2KS6WCV8UJonJSac0RBwuXUotn91mS57Cp2bZuVy0MlDoGkVB1DLk56cuoVtVFZ8p9R//QedSy1e7iK/llCGqu1N1fYyMUM3BM8/o2z4UUrfqlWSqHJPyr9gu2aTi8dAkVFubGSnl2HFlyWW3pNIrs9VGcK4wmUyQJAlnzpyJI3kRnBXbaIIxCsrW1ZGFX1UV3aFovuDzkdW8YwdpdzidVBSkV14gVXCuKuOribGx9G6WixfJp5wIFy6Q73jv3tT2vX17PKH7fGStao31TtCt0YvZWXJ7pSNyNzZGBX2LCcEguQhLSzO/74Vy3TDGbADeAGANb/+fnPM/Z4xtALUPtAEIAvhdznmcQAhjrBPANIAQgCDnfJ6TXxceRUVFOH/+fFSev8lkwooVK5CXl4fp6Wnk5eWlpmZpMNCFNji4MNkcfj/5mcfGyFq3WKik/+c/z/zFqhVcVUMwSG6jQIAmwjVrKLYgJtENGyitMxZer5zvnggHD9J+kwlsCeTlyW4rJcrKKFNFzYIsKEitcG0pQ5KAXbuAX/96oUcSjWCQMoqWLctuS8IMQY/rxgdgH+d8hjFmBnCIMfYcgL8A8BXO+XOMsQMA/hbA3Rr7eAfnPOtnY7G6boZj0rocDgcqKipgtVojj7RgsZBlPzo6t+ycVJCfT0QXCJA7RZJoGTs5SWT50EOU3XDtWvpFT7Goq9PfO5Rz+ca75x4a69atciDV5dJenl+4QN8vWSzE56NV1bJlFChPVNr+4Q+r7++uu7THUVQ058BhIBCI0n5S3hux94l4buUcRmg3OuIAmCTBVl2t7scWdQaxK1Oh1SRE7wD5+/X0yLUMkkS/l3KSEy6zd7wjWrlVqeAqMpVig/TZht+f+aB5lsavp5UgByCuVHP4wcMPV/j1AgDxWsDzDMYYtm/fjpH8fBhsZjRt3x73fqqTgdVqjRKTUjtmpHmzDlgsFpSXl4MxhomJiTi5WV0QF7+40PLz6WJ3u7Pr2zUaKQ869uKuqaEg7dgYkaDPR8Gmjo7M+DG9XsrAUZGb1gRjJAksNGVcLrLm1RrB2O2U6z0zQ8FWPVAGc3fupM/NzFCKYShEYx4bo3hGY2N0T1YAePpp4Ld/W301Nj09Z7eAwWBAb29vxt2ZZrMZK1asSP2D69fr266vj1ZgsUinsllA7z2Rzr2zSN3FsdAVjA03Bj8FYDmAb3HOjzPGfh/AC4yx/w3y9e/U+DgH8CJjjAP4Duf8cY1jfAbAZwBqsZcujh07hsqWRyB53bgWkw63Z8+eqCYietDc3Iz9+/dr3jDFxcW4fPkyWlpadN1UFRUVUZIIY2NjKCoqipKejcq3F4Su7EqkjPILOBxEaJOT2XHllJSQ9ar2HYNBUntUple63USsKZ5vVQjJYDVlSi1wLru2iouJzLVkj2tq4lNDU8GRI3Tu3/c+eeWRl0crnZERKpay2WT3UF+fXFgUC1HNPEcYjUZYrdaMK7pmvfp8oWVMxO+kFwskpJgqdK0POechzvkGALUAtjLG1gL4HIAvcc7rAHwJwL9ofHwX53wTgPsAfJ4xphrJ4pw/zjlv55y3l6lpoOgbZ8L3R0dHsW7duoQWeixu3rypngkTxtjYGJqamnSRfG1tLcwxxSOcc4yNjWFgYCDSTShKhVMsS0Ual7JkPxZCyyYbgTwhFasFtVVJpoNoly7ps+xMJmocEgzSykKStCUatm3LTBDZ54tPO/V4aCUhVEeXLSO31rp16hZ7QQHp02fIP58NUs6KaqYSC+l+ZSw1kgdSrwpfIKT0q3HOJwC8BmA/gMcA/Cz81n8AUE1Z4Jz3h/8OAXhaa7v5wKVLlzA6OorVq1fr/szGjRvhTfLj6+n9WlpaqqthRCgUivT/VLyY9HMRGI1kwRYVyd2nMgGvl1riJULseU3VJZUMPh9NfNu2Jd6uqYniF83NNDlpdfRpaUk97TOREXLkCJ17JVnV1tJqoqyMfo/6epoAHY5okrBa5Q5EGULG9Opj9plVZMMVkq0x5+VlfmJawKybMgABzvkEY8wO4B4AfwPyyd8FIv59AK6rfDYPgIFzPh3+/12gIO6Cob+/H263GyUlJUmlgu12Ow4cOJCwwTcAlJSUIJjEXZKKHz5fadExRiQQCukvHBEiXpkUUfJ4aBJZsYL8zmp50VarXOYPUID4s5/VX0wS29BcBNdiC1JsNtkiVstDdrspQO3x0KRw4YJ6Q+ubN6kcXU+evtlMOiwWi7ZmyvQ0SR08+KCcKXTqFBVomc00Hq+XJiybDVi7llxRBgO5xjKMZcuWhYc1ja6urozvPytYSIs+1WMXFWUmBjUP0GM+VAH4fthPbwDwU875rxhjEwC+yRgzAfAi7F9njFUD+C7n/ACACgBPh60AE4AnOOfPZ/5rEPQGWicnJ7F9+/aERL93717cddddGNGROvXkk0+ipqYGq1evRlFRUZwbx2Kx6HYXqbaKMxpTF/rKBkIhItD+fv1Syek0QE+GigpSOdSDnh5aibhc8bo5whVms5GVLbpkKVFVRT78ri7K3NGTQx/bRUjIS5eU0DFtNlptCKnj0lI6T0IhM8MWqNPpRE1NDfoyoK+S9cy2hXbdpII7JBAL6Mu6OQdgo8rrhwBsVnm9H8CB8P8dANrmPszMIxQKaWbhtLa2UvaOzvzYQCCAzs5OdHZ2oq6uDnv27Inab35+vm7fZlFRUfzymDGy+lK9sO40YSy9GB4mV4heNUKfj4jc5ZJJuKWFSPz4cXp/1y558mCMZBvy86laVqkAmiyNtakp3s974wa93tQkpwi6XCSru2IFuQDEOMOSyBxApn69UCgEm80Gl8uFqQVqZbck4XDcMcHYJSWBoBebNm3C2NhYFBkbDAZ8+MMfBmMMdXV1kfZ/qaKnpwdPPPEE9u7di7q6OkiSpNsKMpvN2i4ei2Vxy9zOF8TKKNXsjMlJOfXR7aYViVIM7fBhcvOYzRT0jZV3BsgtpVZspURrK/n9lZPs8DDwwAPRufGzs3SswUGy7gE5puH3Y/D2bVzt7ITL5UJBQQFKSkpQoHSLpQCv14tbeusQFhrZsOizsc+CgoXTnkoDS0oCQQ927dqF06dP42ZMk2hJktDU1ASn05k2ySvxxhtvRIK4brc7QvYiQKZ8GAwGGAyGuIycKBgMd0yEPysoLiaSPnqUhMmSxE1UMTtL1rbdrh6EPX488b4nJ4mUW1u1j/H88/GZQdu3y1a7QDBIPnu1FYLZDLfPh5mZGfT39+Py5cs4pze/XwUOhyPSlH6uyLrrZiErglNxjzocC6comwbeVhZ9fX19wsDqs88+i3vuuSdjy9vXX38d27dvh8lkQl5eXkS0zOVypZemZrWq69sEAupLyDvJdZOfT2QoKmxjMTNDBVBzBWOJM2f0IFE3rTVr4rN81qzR3l4EmpW/FWPwxpBIqtZ8Z2cn+vr6IvEiv98Pp9MJR7ImNguNbMR09CKVVeLgIKXLZsAonA+8bYi+uLgYg4OD0TnqMTh16hQ6Ojrw6U9/Oj7FMQ2MjIzg4MGDeO973wtJkjA+Po7x8XE0NzdnzMICQFahmqthnpsbzAmdndRoYvduspYkiVYxeXk0iZ0/n7lj9feTK+WVVzIvHdHUJGfctLWRJEQi8kqBXK5cuYLCwkK4XK5IwN5oNMbFdILBIM6dO4cf/OAHUYkBv/Vbv4UWIY/7doLeVUgqxpckZSdBIqdHnxxaOb6bNm0C5xxv6aiqHB8fT5o3b7FYUFNTg/HxcUxOTmouZ51OJ+699944v3t/fz+WL1+euZzk/HzyK89XoC3TF2N5OfCb39D/hw5RJamoxH3xxcweS6CriyptJyZS07svL4/27cdCxFHsduB3fzd5AN1oVF15xV4bPTF6OmvXrkVpaWnEQuecIxgMYmJiAhcuXMDY2BhsNluUWuq1a9dSJvr+/n7cunUrEmtyuVxLd7JI9X7s6aGAPmOZC8oulNbNnQS1xh0mkwkOhwOHDh3SvZ//+3//L+6//360tbXFCZIZjUbU1NTgdtiCXr58Oa6r3PhFRUV4+OGHVQXLPB4PphX5tw6HI6Vq3TgYjSQbfOjQ3BsgzzdKSuLb9CULeGYKU1NkxT30kFwdq5SaEP8LS89oJLfS1q30mngohb0cDsqkqawkX78oIhM1ArE1BQ4HQqFQZKUpSRKGhoYwmURT/8KFC9i0aRPy8vLAOcfMzAzOnz+Pw4cP4/Lly3jrrbdQVVWFwsJCOJ1OTE5Ooq+vD1euXMGqVauiYkaJ/O4OhwNf/vKXIxPGnj178Fu/9Vs6T3AayMujblxKWK2pWduxZBkKqX9eKcYm/t+7V25WLpBssjab5Wp0LaL2+dRrOcTxAaArBZXWFLGkiL6ioiLOJVJSUqJKxIkQDAbxi1/8Ar/4xS9wzz33wGazYePGjbBarfB6vRGSB8jSam1txbVr16L2wRhL2CO2U1GgwxhDcXExKioq0if8qipye7zwQnqfTwWZtOj7+uQmJQuFyUm6sTPpHgKAb32L3ERJBMpCfn9k4meM4fLly7p2Pz4+jtnZWczMzODatWt49dVXMTo6ioGBARQWFmJgYAB+vx99fX3o7+/H4OAg/vEf/xGMMRw4cACbN2/G888/j6KiIjQ1NeGTn/xk3OqzsLAQH//4x/FP//RPAFKPFaQMkyn+fPl8c8tZz8vTP1E4HLQq0+vSY4z2n8wSV1P1nEcsKaJnjKG5uRmKvvAIhUJobGzE8PBwWkp+vwm7FC5dugSr1YqHHnoobpvu7m60trbi5s2bsNls2LZtGxoaGnSTNucco6OjmJiYQH19fUJtnYSoqFDvprNYUVwMvPbaQo+CisA4B9rb5faBmUAgQFWxL79MvnsdSEWE7NatWzAYDDh27Bhu3bqFqakpTE5O4uLFixgdHYXdbseaNWvg9/tRVVWFkpISXLlyBdu2bcPp06dx+PDhiIvo5MmTuPfee1XdMhUVFbrH9LaDWKUt8sSHJZdeuXz58qjnIyMj8Hq9WK9XJlUDHR0duHz5MoaGhlTf7+7uht1ux+rVq9HU1JSWxnwoFJqb2qBoOZhtZGIiMRrJXaJxPucdQtBq167M7nfr1pRE5lLJ+JIkCadOncLp06cxMzOD2dlZ+Hw+jI6Ooq6uDhs2bIDRaMS5c+dw8OBBHD9+PBJbEgJ/BQUFcDgceOGFF9DS0hJ3fUuShGeffTbyPOtaN4sRyb7zHVDfsuSIvlkUnyhw4cIFuFwubNu2LaE7RQ8S5TMHg0GcP38ezz//fJQPPhXoET5LiDvF+ioqmj9ffCKUl1Oq3MgI+dR7e0ldcq6w2ajX6U9+AjQ06P5YRUUFNm/erOs6NRgM2LhxIz772c/iU5/6FB544IFI57Lbt2/j6NGjGBkZiSLnGzdu4OTJk/D7/Th48CB8Ph8OHDiAv/qrv8LHP/5xfP7zn4+sDAKBADjnUeS/WNt1ZhXJVuZ3ANEvKdcNAKxcuRL5M0EY7Ras2LMn8rrRaMSyZcvgdDoRCAQiWQTiIaAMUsVWtRqNRtTW1kapVYr3YwNax48fx/bt2yNuGBH0YowlFDhzuVya7+lCNvpYZgOL5eYwGOJXFWYzBeW09OsF9u+nfrn/43/IrzU1UZ79f/kvpE3/xBNUAzA+Dtx7L2UUJYC4RlauXImLKWQDMcZw7ty5SGtKIbJ38+ZN7N27F2+Ev4s/JiWwsbERTzzxBNyKIP6u8KrmF7/4BWZnZ++cqtpsgXMi+0SN3JVdrxbhqmfJEX1dXR0clwZgkkxYuXJl3Ptp+78VuHr1qq7trly5Evfa8uXL8Xu/93uq24sq2TmhtpY6F2UKyglMXMj/63/NbZ/FxZlpSJItBAJUtapERQVw9930nt9Pjy1baLvf/31ZnMzvJ1KYnqZVglIvKQVr2OFwwGq1Jqz7UOLq1av4l3/5F+zatQv9MZ24Dh48iA0bNuDMmTNRr7e0tODatWtRJK+ESNNUwmg04mtf+xoCgQCuX7+Oj3/842hvb4fdbsfY2BjKw1LWWdetn08kc1UGAvR7m0x0j1gsi665+5Ij+sWORKlsnHMEwhWRRqMxvZtlPm6wufjoy8vJyl0s0LK+Tp6UrfPGRrLQ1TIxAgG5I1SSlEit86bmDuGco6ioCANJ9PItFgsGBwfx9a9/HRs2bMBhFVVPzjn6+vpQVlYWlS5cUFCQMCPtT/7kT+JeM5lMmJ6ejqQrf+UrX0F9fT3e8Y53YGZmJnLd7gmvpltaWsA5h9VqvXPIX+2aUL5mMESnyjJGKZyhEK0Gg8F4ohdKpak2NskQlhzRd3d3Y2baDxb04vibx+OIddu2bQsaUOrr64ssz9Wg1LVftDdGKn7asjKyemdmyCqeL5IXbfxEjrt4iHNqMNCNl0gzp62Nep3u2ZOZUvcXX6Qso7Y2alye5PdNFucJBoP4yle+AovFgqGhoYg1rYbh4WGsWbMG4+PjCAaDKC0txYVY2WYdiM0kq62txfbt2zEelq0WhsoL4TRfh8MBm82G8vLyhbme0zkmY5RmKXozm83RtRIAvefzEYELcgc0C+Cicu0XAEuO6D0eD2Y9PkheN26qBE6FFn3W84E14PV6ExK9wB2f3cAYLWd/8xuyeFetojTD+Ur9HB8nqzwRRJs/Lbz8MvBnf5Y5PRPROP3UKVre33VXQn+uxWJRdasYjUYcPXoUTz75JJqbm/Hcc88BiK7NUMPFixexe/duXLt2Da2trSkVEaph3759qKio0HT9rFy5Ena7PZI+XFlZOafjRWA0ygQuzp+wqBkj8hWkqlUslQiiEA6QhQSV1rv4q5S20JNKvYD3dNIzwBizMcZOMMbOMsYuMsa+En59A2PsGGPsDGPsTcaYaotAxth+xthVxtgNxtgfZfoLxCJZVkB3dzeuXbsGj8cTeXi9Xni93jln5OhFVkk81vJYKBQUUKMOgAj/xIn5ze/Xoyw4O5s4I2ZqCnjqqcxbYtXVlHUUjvVoXQ/Nzc1YuXIlzGYzGGMwmUwwm814/PHH8eSTT6KgoADHxDmGPmXJQ4cOwWQypU3ySqu8vLxck+QBYMOGDZExSZKEUCiEUCiUknQ3AJm8zWZahVks9FwQutEov2e1yr5y8blMIBP3VV0djd1upwLH0lKqDC8qokcWoecs+ADs45zPMMbMAA4xxp4DtQT8Cuf8OcbYAQB/C+Bu5QfDXam+BeBeAL0ATjLGnuGcX8rkl1CCsgoS/yBnzpyJC0wBwGc+85nsDCoGWbfos02oycZWUQH86EfZHUMyVFQQASSy2IuLkwtTHT1KAmip6t8nws2b9LBYgC99SZWMenp68MMf/hBdXV24dOkSzGZzxC0iUFpaipOKVYvb7UZDQ0PStoGxwdpUIUj6ySefxMc+9jFVWe+HH3446houKCiIGr/RaEwsy62EcJ0kwmIwbpLBYiF5DElSn4AMF7P2PZJa9JwgpBzN4QcPP0QuYAGoh2wstgK4wTnv4Jz7ATwJIL60NIPIul72HOFyuRKSuNFonNvKYrF8/4W+8Xp6yLeeCEND+uoOfvazzIypvBy47z45eOv3Ay+9FLfZ+fPn8dGPfhTPPfccLl0imyiW5AGotgasjtXCzzD0GCB1dXVJ5ZBj05qTHFTfdncCDIaMNoDXC11HDFvmpwAsB/AtzvlxxtjvA3iBMfa/QRPGTpWP1gBQyu71AtimcYzPINx3tr6+Xu/47zgUFxcnvFnEMj0tzBfJJxvfxARVhMaKlc039FiMoRDpisf6YpWCZU4n6ZnMRTDOYADe/35asvf0yP1rz52DtG0bYDDg9OnT+Pa3v62alhsLpW9eiTlVVutALDkHVXLL161bF7Wd2WyOcvmYTCZVeeUcsgddUQrOeYhzvgFALYCtjLG1AD4H4Euc8zoAXwLwLyofVfslVdmIc/4457ydc95eNh9l/AsENcvsjkNpqdwkOz8/viuQ07nwJK+39eKxY9T8OxCgRzBID7+fUuE8HpI0niuB3nUXkTwAHDggTyzBINjzz+PZZ5/FK6+8EiWYJ2AymVBYWIgVK1ZEpDXUtgMyUHCXItSI3mAwRJF4bNGhyWSKen96ehoXL15MKg+eQ/pIaQ3BOZ9gjL0GYD+AxwB8MfzWfwD4rspHegHUKZ7XQt3FkzGQ3y+D/tQMI5lfck5WTjYEzQTZ+XxyOzzhs16+XF6G3rwJnDtH2wpt+fnEhg2kPil86X6//o5UenSJfvhD4JOflFNL9TadMBqBRx+Nbj+Ynw/s3EmTYW0tTJxj25Yt2LZtGz784Q/j6aefxk9/+tPI5uvXr8fVq1dx/fp1rF+/HpIk4ZVXXtF3/CxDzXB59tlnYTKZ8P73vx8WiwUOh0Pzuh4dHcU3vvENAMBXv/rVrI717YykRM8YKwMQCJO8HcA9AP4GRNh3AXgNwD4AapUXJwG0MMaWAegD8CiAj2Rm6OqgPN/FS/RZz+zJJNn391P+u9hfbEGQ0te4fDnw7/8O6JTY1QWRXSEkXkMh+quWWeV0RruUUpFY0HO+JAn4btiW2bYN0Gq+YTIB+/ZRDv/p07TtihXx273znbQdAP7kk5GXbTYbPvKRj2DHjh340pe+BJfLhb6+vohL5ubNmzh9+rSmfztW4iAbEKRtMpkQUglSS5IUyX5LVPjX3d2Nf/qnf8Lk5CTMZjOCweDctZ5yUIUei74KwPfDfnoDgJ9yzn/FGJsA8E3GmAmAF2H/OmOsGsB3OecHOOdBxtgXALwAwAjge5zzFNr5LD3MVwpnRuD16p80OAf+4i/I8r99G/jqVymXPR3s2EEpmYmOpSyAYoyOtX27rE8jSZS7L9Lv7HbKm1frWJVqnvXx42Shx8pDvPvdJItgNlOc4soVYPNm7f2ECVNqb4chnHZIX4+jsbERP//5z+H3+/Hoo48CAJqamvDKK6+okvzevXtx+/ZtnDhxIrXvMgdUCVeUCsRk4PF4YDKZ4gj87Nmz+N73vheZmAKBAE6ePBnR2ckhs0hK9JzzcwA2qrx+CEDcVcw57wdwQPH8WQDPxm6XLeh1ffy3//bfUFRUFHXTOJ3OpEQs9p8sYyAYDEYyJpSYUycpvVgoTXrOyQpvaAD+z/8hV0dZmdxDVS+SEa+yM5DSopSkxMVNotuTEps2URVkqvjBD+g71tZS56DVq6M15wsLZQ2cJLC2tKBifBwDAwOQJAmBQAB+vx+BQACDg4PYvn07rFYrLBYLamtrYbFYoizlQCCAEydOwG63Y9euXbhx44amDz+TSNT3WOs+5Jzj5z//eaRyVomOjo4c0WcJS64y1mg0wmwyg1ssCRsmlJWVxQk2pSstrIW1a9fGlZnPi8zrYkixNJvJr805uYCOHAEUfueEyFY2BucUeDUa5WKbmZn0O0t94xvUhvD3f1/9fR0kL1BUVIR//ud/1nTLzGp0PDIajXjqqafiXDYrVqxARUUF+vr6cDPDHbzUArCxUCN6zjl+9rOf4fXXX1f9jFq6aA6ZwZIj+lAohEAwAMnvx9DgoOZ2J0+eVNWuzyTUqgYdDge6urpgNpsRCoVw/vx5uN1utLe3Y9myZbqKqZJisXSZEmOorgZSOdfZInoRPxDl8oAcYE4HXV3At78NvOc9ujtIJYLP50upDsRut+OHP/yhqp/86tWrEZXVhoYGNDQ0YHh4WHebQi34/f5INa7esRqNRoRCITz++ONxLTeVGBwchCRJi1fj6Q7GkiN6vbArdSqyhImJCaxduxbDw8OoqKhAKBTCrVu30NPTg2AwGNWYuaurC+vWrcOuXbtQXFy8OHz5mSTclSuBr389WvXvK18hn34s0r3R0xlvXR19Ll2r1++XC6AyDGVvhFiYTCY89dRTqiQfi66urki1bGVlJVpaWjA5OYnz58+nXGCo3F5rdcoYi8iBGwwGeL1efOMb30ja8Nzr9eLs2bPYuDHOU5zDHPG2Jfrm5uaIwFg20d/fD845enrkujGx9FUee2ZmBkePHsXRo0exbt06PPLIIwkblCSF0o+dLjJ5btQ0uk0mdaKfT1y5QhPLunXy9715U59WDkD+/Qw1e1FeDwaDAc8++yympqZgMBhgMBgiVdNGoxHBYDCt4qiBgYGI9PFdd92FQCAAn8+Hs2fP6nLJKA2k69evw+FwYN26dVEtED0eD773ve/he9/7Hvbt25dSv+ZnnnkG69evXxyGzhLC25LoHQ4HXnrpJVitVtxzzz1JLY25IJ2J5Pz58zAajXjggQcy0iglbSyUZs58V0xKEnD2rPy8qooahuvBd76TsfHGdjoT5CnSFfUQsR6YTCbs3LkzylfudDqxdu1aSJKEs2fP6i5eOnv2LNbFtF4UJL17924MJnCfqmFgYAAnT57E9u3bAZChVFxcPDejJ4elT/TLly+Psg5u374Nv9+PI0eORDr4PPDAA6rCTAuJM2fOwGaz4b3vfe/CDSKTRK+2L7udgqHZPEa2kYVjWiwWPPPMMxnfL0D+epvNFmktKDA9PY2jR48CIKt969atMBqNOH/+PGYUv5Ga4XL8+HGUlJRElCm7u7uxefPmtA2oJ598EidOnIAkSbh+/Tq++tWv5oh+jlhyRG+325GfbwKzmlCukvVSXFwMq9WKzZs34/Dhwzhy5Ag6Ojrwuc99LqHk6kJgwYNS2c4Qev/7yXUTCkXrfZeUxBMo55RbL8YkSeodnx58UP4/EIjWDxd/Re698iHeY4xcTJKkbqnHjisTjcSjds/R3d0d5QrJFLZs2YLz588ntdY9Hk8kH99sNmPz5s2w2WyajUquX78e1amqvb0dwWAw7Qwzn88356BxDtFYckTv8XgwM+OF5HVjSOXCtNvtsNlsUS3XBgYG5qWiMFVsTlRsk20og6aZgNpNLzJxzObo3qpqYIxy5MVkbLdT820tlJUBCTI8MoapKdLezwCKiooQDAbjrO1MwGAwYGpqKmU9mUAggFOnTgHQV+y3Y8cOTE9PZzT2lRM/mzuWHNEnQ3Nzs+qNlJ+fH2mHtlAoKSlBaWkpNm3ahGXLliWVes0qMqm/ngyBABUYJXKf3bypX18GmB8//0c/SgVTGYIkSaioqMCf/umf4ubNmzAajairq8ONGzeitG/Swe7du+c8gYRCIc2VRn19PVatWoXbt28veqnwtyOWHNEnmv0rKytVl59lZWUL7rYxGAz43d/9XeTHKkEuFDJ9sybbX6JAY29vaiQ/X7jvvoxOKMJVNzw8HFGhHBsbQ0VFBbZs2RLVZCQVVFdXp/3ZWMS6Y4qKirB161b09fWhN9UKaJ3IZeDMHW+ryoTW1ta4aliAgl8L7Q+XJCmS9rYoYDTqa8qhBwZDcpExrcIlxqjNWmsrCadVVJAVXVhI8gVamA+LPsPa70pCKygoQE1NDfx+P0ZHR9Hc3Jy2BHF1dXXGdOqFtW40GnHvvfdi2bJl6O3tzaoVn1shzB1LzqLXQkNDA86pNAsHSLNjIbWwGWNgjKGrqwvLli1T3WberRqDgXLfM7WvRD54EWhVg91OImni/cpK0pYBaHKw2+V4grIiONvna/VqigMoIY4tSeSOMhj0tcEL48CBAxgbG0N3dzdef/11+GJqDO666y788pe/hN1u103c27dvj+orO1dMTk6iubkZNTU1CAQCuiefRMVfsduJ+0E8v3DhQkQUTavnrND+4ZxH7pXS0tKIfr/WccR+Yg09o9GIwsLChGNdaOMwFbwtiH7ZsmWorq6OqO2pXWwnT55EU1MTGGNwu91RPyJjDKtXr47S3hYXipKARXs08VDmP/v9frjdbkxMTCAQCMQtgR966CHYbDbVm7KkpAQrV66c83lYtMjLIzLXg4EBsuh7e4nki4uj3xekH/t6prF2bfxxvd742IbfT5o3ymwfDQwPD+Ppp5/WfL+3txcPP/xwROzszTffTDhEp9OZcZ0bUdSXSTz44IOqPZwF/u3f/i2t/X7xi19EZWVlSp9ZtWoVmpqa4HA4klYd54h+ARE709tsNhgMhqgsGy28qtGo4iMf+QjOnTuXNav/scceQ6KuWgsWlM3UkjmZRavHOi0pof3k5xOhLlsWvQqoraUesPNVaSskeru6qDXghg3a24rrxmxO2ORErTVgLG7fvg2fz4cCHZk+GzduzEoGT6Zgs9lw1113JST5uSAVlw9jDO9617t0nVfgzosbLDmij4XIl58rskXy5eXlKC8vT3hRJlLhzCrmQ2nTYIhvaBKL6mp5GzEpCI15xog8Rf57Xl7GfeeqmJ4mgv/Vr4jIBwaA/fu1txdunAQ4r0NF02Aw4MyZM7BYLKipqUmo+LiYfdvr168HY0xVynu+0dTUhA0bNuiSEGeMZVVqPFu/2JIjeuVMu3PnzrRJvqioCOvWrYMkSbDZbJoysXOFxWJJekPOzs5q+hqzivkgCkkCysup+Ck2dc9oJNeH2kQQCtFnVq6UC53y8sg3Lizn+++n7fx+QAThxXcSPn3RsUq5DFfWEIi/YqIXq5PJyWjZ5a4uOpaWpSc6ZCVY7itTF4XioxLV1dX49a9/DYBUJOvq6jSJfsWKFejs7NQ81kKjoqJCVxP0bKOurg7t7e0LOobI/c+zJ2Oup5WgDcAbAKzh7f+Tc/7njLGfABA90goBTIQbiMd+vhPANKi/X5BzntWzKk6Uw+HA6dOnU/688IefOXMmsuw9deoUPvShD2VFJqGmpibpNgtmmc3XcT0eEgdTEr0g20TnXJJkAlbC5yOiFwFgk4maoWhUduqC0v3U1kZuolgkO18+H41Fw5W1d+9eLF++HHl5eQgEAvirv/orxeFZnL/92LFjcT0PGGPYs2cPDh8+rEvZciFQV1eH2dlZOByOrBlQetHb24tf/epXACgYnsjvLrhFnFeLxZI0uHzt2jVcvXoVTqcTTUmkrHnW7Hl96ZU+APs4520ANgDYzxjbzjn/EOd8Q5jcnwLwswT7eEd426xPnYFAIBJ9T9Xdsn79ephMJhw+fDgqr97hcKTUy9JgMKC0tBQ1NTVYtmwZGhsbUVlZCZfLBZPJFEXce/bsSbq/mUzqwaSC+SJ6ux2IFb/Kz0/u0tGLYJDItbx87vvasAEYHo5/XfS3TYYEFtvv/M7vwGKxIBAIwGg0Rpprf+QjH4HD4VC1gH0+XxTZbNu2DW+88caiJPn6+nps2rQJ5eXluHXrlmpj8fkG5xyzs7OYnZ1Fb29vRpsPTU1NRbqGLfTvoaeVIAcgmMYcfkQYgNFV9kFQg/AFRyAQQJ7DhcmR1HLSV65ciZ6enrjq2M9//vMYHh7GkJoFpwGXy6XaYMFgMKCsrAwNDQ3o7+9HMBhUXaK/rWA2k1sldlJRc4E4HOT+EAFXi4VcNZxTsFaS5GBnrGUWDNL2ra1UZZvqOWdM25IHyIWkB5Kk+t0451EFR6FQCKtXr8aaNWvw1FNP4dChQ6q7u379Ovbu3Ys33ngDmzdvnteesamisbExKjh83333aernZAKproSPHTuG5cuXY1Oi+gyduHnzJiYmJha82l5Al48+3Bj8FIDlAL7FOT+ueHsPgEHO+XXVD9Ok8CJjjAP4Duf8cY1jfAbhBuP19fU6hx8Ps9mM6ZnUZ2W32636o0xMTKSsg1NUVKT6uiRJGBwcxODgIIqKinT73RdsIsi2RW+xkGsm1rJzOOKt5rw8SlMMhYjUPR4izYICIvepKSJjEYiNLcAaGqJiK48HWLECSCUIaDAA69drkzxAxVx696WAkAUeHx+Pcw0Ka/M///M/E+5yfHwcu3btykjSQbZgNpujXE+MMXSLeohFhHTdpMJoE6urW7duLXi1vRK6iJ5zHgKwgTFWCOBpxthazrmYij8M4McJPr6Lc97PGCsH8BJj7ArnPC7nKzwBPA4A7e3taTMMkXVqGu7CIlLDsWPH4vS2EyFRYZYS4+PjukXLsqmXnxDZJHqRd662fA8EqGuT0IS32eghqmuVN5DfT49YXylj0WJjnBPJc04TxLZtwI0byXXnTSZgzZrEJG8y6W8+wjkuX76MxsZGTExM4OrVq6rV2rQpx9NPP53QBVlYWIjz589j586d+o6/AFizZg0CgUBklcsYwwMPPJC1tMr5glDYFL0DlL0jYieMhc65TynrhnM+wRh7DcB+ABcYYyYA7wOgyVic8/7w3yHG2NMAtoKCu1lBMr9fQ0MDQqEQhoeHUV5ejvr6+oS5xjdv3sQ999yDUCiEEY3qzoKCAphMJrhcrojSXzJUV1fD5XLpirIvWdeOVoaKcMfU1tLfQCC9/Hgl+Sv/LymhgqvGRpo8pqbUJzWzGVi1St0nr8Sjj+qXXPB6YWFMV858R0cHnnrqqYTbOJ1OTE5O4uTJkygrK8NwsrHOIxhj2L17Nw4fPhx1nd97773zQvLZTmIYHx+P+l6LxU2jhqTTDGOsLGzJgzFmB3APABEVugfAFc65qpoRYyyPMeYU/wN4F4DsOeWgTfQlJSXYtWsXurq60NvbC5/Ph56enqjlcnV1ddRntm3bht27d+PVV1+Fw+GIFC6VlpaioaEBjDG4XC7YbDZcv35dN8m3trbii1/8Iu69996EFyNjDC0tLUuzKlYQY1WVes9VxigYazKl30NWC8KCHhkhHR21HqU2G7l4ksknP/xwyg3Ga3RWa7700kua7xkMBuzduxeDg4PgnCMQCCyq68Rut2PPnj04ePBgnDEzX5pO6cgbL+bag7lAj0VfBeD7YT+9AcBPOee/Cr/3KGLcNoyxagDf5ZwfAFABcvWIYz3BOX8+U4NXgxrRG41GtLS0qPowL168GCmTHhkZQWVlZUTC4K233oLf78e2bdvw1FNPYd++fcjPz8eJEyfQ1dWFtrY2NDc3pxwA++QnP6nLSs8LE4jI1ElLl3shtbxFfrvJBNTXk1Uu0iaV2jT5+XIOvGj4oXzk5ZFrJBhMLo4mkCgDRnkzixROg0HOiMnLI2tfw6UShTSExhhjyMvLS+rDVfYZLigogM1mQ21tLRwOB/x+f9xK9ODBg1i3bp2uwqtswuVyacqBA9QesLKyMusuyXRIu7u7O+JKEzImgg927NiBgoKCtO7DhdbU15N1cw6Aalt2zvnHVV7rB3Ag/H8HgLa5DTE1qLlCdu7ciYMHD2p+RmlhqFkboVAIwWAQv/zlL9HW1oauri4AlPao14oXqK6u1v2jGwyGiITDglwohYXx1nZsR6ZYxHZtUuLWLe30wjSVGTVhMABasZLYsXFOqZcDAzSO6mpA7zI8xd/FEwjgOQ2pDYB+89nZWXz729/G7du3sWXLFng8Hly4cAGTk5NJe7Devn0bFRUVKfdqzRRcLhfWrFmTUA+nra0NtxI1jckQ0pEOmZqa0syw6+npQVNTEzZt2gSr1ZrQ7epyueD1eiFJEkwmE5qbmxd0tbDkKmOVJ19Ut6ar92Gz2SJNil0uF4aGhqJ8i2vWrImQfiLs27cPbW1tKC0tBWNMt8+dMYapqamIZT/vMBqzrwKZTZSUUJZO7CpPWbQkVhaVlTQ55OfHV+hmEFoNvoWY3ic+8YmobVLVkR8ZGcHKlSsxMTERp36ZbeTl5WHVqlUJSX4+c+iTqU+qwel0Jgx+d3R0oKOjA1u3bk24H7/fD4PBAEmSInU9C4klR/SMMTidLsBqwmAopIvkq6qqcFtFPXHbtm2wWCyYmpqC3+/HypUrwRiDx+PByMiILl9jRUUFVqxYkVC0TA1WqxWcc4yPj0dUN3NIAZIEiKymoaFoX7vBQBLDysCl3w/U1AAJtGNU4XaTP18ntMSwOOf4zne+ozkRJMPGjRvhdDpx48YNBINB7N27N6GPPxtobGzE8ePHE26zZcsWXVlpc0W2V8GhUCgiX7IYCr+SYckRvdFoxPTEFCSvO0o7RDQ5ZozhwoUL2LhxI/x+P/r7+zE4OIi9e/dGLo5AIAC/34/Ozk50dXWhpKQELS0tsFgsURPH6tWrk44nFArhwx/+MD796U8DAAYHB1FWVoYHH3wwIfkHAgGEQqHI8nN2dhZGo3FhNG+WEvLztSWR7XbK4bdY4puIa2WzpNgRTEk+jDFYLBb8+te/xuXLlzE6OhqxApN/jfxIxfTOnTtx5MiRqPcHBgZQXl6eUqHfXGC1WnVVomdLyyUW2U5nFC7b+vp6FGdbEjsDWHJEr7Vc3bBhQ0Tr3Wg0xln64vmyZcvi/Iejo6MYHR3FmjVrIq+ZzWZ0dnYmtbaFlsd3v/vdqNd/9KMf4R/+4R+i9qmEwWBAMBiMLPmCwSC8Xi9MJtMdJ5G6YDAYgCtXogO4iay8/Hxg69Z49ctERP/CC8B73pN4HEYjpWpKEgI+X6SjWU9PD773ve9FNqupqUFJSQmqqqrw8ssvx+XXFxQUoK2tLZJ/39jYiGXLluGtt96KO+TMzAzKy8sRDAY18/Qzic2bN8dNNmrIhl6UGhYyb13ZOGWxYMkRvdYJVmYiaPnIbTZbQnfMxMQECgsL0dDQgHXr1sFkMqGjo0Nze4fDobmU9fl8+OxnP4tHH30Un/zkJ2G322E0GiNNSfr6+mAymTAyMgKXy4WioiJ0dXXBYDBg9erVMCeRvH1bQ1jigQC5aEIhKs4ymRIXgblcRMixRM85sHdv9Gsmk2zNWyzyMZNMwg6XCz/60Y9UrxuxAu3q6sLy5cvhcDjQ29uLGzduIC8vD6WlpVEGSmdnJzo7O7F582bVpICOjg60trZiZmYm5eruVLBixQrd/WKPHj2K/fv34+LFi1kbD5C6XrzNZoPdbteslUkFfr8/o5o5mcDbgug3bNigq0Bj69atkRvJaDRi165dCAaDcDqdKCkpgclkgs/nw+3bt3WVb2vJKghwzvHjH/8YLS0tqK6uxs9//nMcPnwY7e3tKCoqwt69exEIBPD888/jwIEDqK2tjXwuhwRgLNonL3TqAfLdC2KuqCBi9/nktolaKY+xK8W8PKClJeWhmcxm3e6L2dlZ1NbWYtu2bejo6FANcm7fvh2jo6OwWCyqZH7t2rWsyiPs2bMHx44dS8lPPR8r0lQNIYfDgRs3bmRlLIvhfl1yRF9aWgqb3w6L3YKWXbui+kImwvbt2yNLXJPJhMLCwgjp79u3L+UO92VlZfjZzxIJesp46aWXom7iV199FWazGevWrUNXVxfuuusu2MIBP0mScPv2bTQ0NKQ0nrcVQiFqJajmsrDbqUgrFJIfABG5yzUvHapSufFnZ2dx/fp1tLS04MSJE3Gr0YsXL0KSJFitVmzfvh0HDx5EdXV1xLUIICvWvMvlQkFBQcK0ZS3MR9xgvla8fX19KCsri/pdrFZr2hZ9thw+S47ox8fH4fU6Met141oKVozZbAZjDGvXrkVBQUGUBSS0s5UZEUVFRXF5upIkYWpqCjMzM/j5z3+u+4ZWs3ACgQBeffVVrF69Gh0dHTAajRgcHITRaERpaSlcLhdCoRDsdnvS9Mu0i60yjZISdREzi4VI1mIht0kwqN0sXC+MRrLYBTin3PjZWW3lylTOUSAAzMxQxk2KHYfSsfA6Oztht9sxMzOD6upqeL1etLa2wmw24+jRowgGg3jjjTdgtVojzUi2bt2Kq1evRoi1oqICLS0tYIylRdAAEfyGDRvgdrtTriERuHTpUiR+kC3MB9EbjUZs2bIFoVAoiujnEnDOdZjSiXQvnkQX/rFjx/D+978/SkfE4XDgxz+O13L7xCc+gSeeeCIlfRqtYJnD4YDH40F3dzfy8/Mj/Szdbje6urrQ3d2NTZs24eGHH9bcN+ccfr9fV5OErMPlogIst1suSCotJStbwG4nf3omfJzKa8FoTK5vn0r/glAIuH6dJqfSUsrWUZNyyBACgQAefPBB3Lp1C8eOHYPBYIgkF2zbtg2Dg4Po7OyMSkY4ceIEli9fjtraWlgsFgwNDeHQoUMoKSlJawx2ux3Lli2bcx9at9uNdevWqQaRM4VMEr3IjhL3f3FxcYRnlJlG4v7inEfaDS4WnaolR/TZ8ofFkmSi46RSqFJVVaXZN7OmpiZyQV26dAk7d+5EXl4e/H5/JDPo2rVruHjxIsbGxjA7Oxs3zk2bNkU62mez16VuiAbfYhWiNvnYbNqul2T7tlqJhNV+H+GHt1qjK3QZk9sPjo+rK2oqYbdTc3Ih1xAKUeMUnUSfzjVaUFCAZ555JiKboLQajx8/jurqajidzjiXgXD5zc7ORiQHZmZm0NjYqKvV4MaNG+H1elFYWAiPx5MxMbLLly9ntRdDpq713t7eOEOMc47S0lIEg8FIvUsoFMIzzzyD0Rg11EceeWRRkP0iuPMzi2wFepxOZ8RFwjnXVAnknOvSMQFIaE3rIjCbzTCbzZG8eXFjj42NRR17ZmYGZ8+e1fR7tra2RtxOSr3sYDAIxtjCpWomW104HGSFj45S4DS21SBAE4LZHK2JY7OR1IIyvU5kw6xYgYSQpOQiZgDp4IiKWjFWvTLFSI/oJycnsXv3brzwwguq7/f392PXrl24ceMGVqxYgdOnT4NzHmlfV1VVFXHp+Hw+1NfXJyX6jRs3Ynh4OOX4VCI0NzdjzZo1GBgYyKrSZjYDoF1dXZAkCQUFBThy5AgmJyc1z9FiCMQCS5Dos5U/+6//+q+6tguFQrp/3BUrVkRyj10uVyTL59atW1i/fj0OHz4cVfS1a9eutIWgRDszk8kESZIiLRcXbU6+1UoP0UXK6ZQbjxgMROoipVGJYJAUKSVJdt3obQM5M5Ow1V8EsccUWTtZ9gtbrVbYbDbNwqTDhw+jpKQk4qvfs2cPhoaGMDAwEMnYEjh27Biampo004OFKmsmSR6gTm7ZdNkIpOKm7OzsxPnz51XdvlMachg9PT1RonOxeOSRRyL3mSRJuhqhSxJHKJSduMWSI/qFJq4rV67oangcm/I2NTWFU6dOob29HZIk4a233opanq9btw7Xr19XnUQS9bNVbi+yLwKBQGS5uWgCtVoQTUcAfa4Rk4mIXnnT+nwkcZAMXi9Z/sJYsFjITeN2JxZrmycMDAzgrrvuwvDwsGbje+E6aG5uht/vB2Mskm9fUFAQMRSSVeCWlpYm1KxJFU6nEytXrsTMzEykqldZ3ZtppOKjHx0dzWje+/r16yOxMQFPbG2GKrLHXUuO6BcSLpdLV3792rVrVfOaGWPwer1x7pyCggLcfffdMJvNcDqdcWX0idLnYm/m2G09Hg/MZjNMJtPiJvxUEDsZ6l0+V1ZSgVWqMJmybs0LuN3uqJZ8ajCbzfB6vXjjjTewe/fuyOurVq2KBHDb2toS6tJkonBIienp6YhA29atW2EymdDY2IjBwUFMTEzoJMLsINNegIaGhjkIymXnHlzY/lZZwEKS1Zo1a5LKw9bU1Ghm2UxMTKCjowODg4NYv3595PXJyUn84z/+I375y19icnISExMTkcf4+Pic85IDgQA8Hg+8Xu8dIdCkC0oLXC/Rp5sWN0/6LQDFdZK575S/oZJwlAbE8ePHsTxBn9upqSmsWrVqDiPVxokTJ3Dr1i1wzuHxeNDU1IQNGzZoZgOVl5enfIxUfOOLIkkhy0j6DRljNlDrP2t4+//knP85Y+wnAER0qxDABOd8g8rn9wP4Jmhd8l3O+dczM3R1LGTwI5nLRgShYiPzAj6fL2GALNuTmCRJS8OqV0oRuN3aefNKGI36m5rEYp6secYYfv3rX+vaVlxjShG8M2fOoKSkJPJeMoLTanKfCczMzOBXv6L+RefPn0drayvsdjva2toiqxZxLxcUFKCoqCjSm0G8fvnyZU3LOZVc9tbW1oy6qRYj9ExlPgD7OOczjDEzgEOMsec45x8SGzDG/l8AcWZGuCvVtwDcC6AXwEnG2DOcc/V8wjsYiRqMA8COHTvmfDHNxyQmgrYL1uwk0wgGs9vkHKCg8TzA4XDAZrPp8msXFRWhubkZw8PDaGhoQFdXFwKBQFQCwPT0dEKFy/lc3d28eRMWiwVnz54FANTW1kaaittsNjz77LNxn1m9ejVqampQUFAQF+BNhejT0a3PHrJzrerpMMUBiCvLHH5ERsOIDT4IYJ/Kx7cCuBHuNAXG2JMAHgKQVaK3Vq+E5E+h+GWO2Lt3b8IGESUlJSnraGSKZFPdD+ccPp8vosAnlBbvWBiN+lwrwSD52rVy8BNhHiZEo9GImzdv6g5e2u123Lp1C+Pj42hpaYmQvdKK7+vrw65du1SJvqWlZV6yYwRCoVCUn763tzdpxo+oP3nwwQfj3kuF6DMdEO7v74/IUKQuP5Gda4npsRLDlvkpAMsBfItz/oeK9/YC+DvOebvK5x4BsJ9z/unw898GsI1z/gWVbT8D4DMAUF9fv1lP5yY1/OY3v8Gnf0PLuSKDF1znDMm0TjBLvl1UBoNqdz0VvR3xNHb78OtGkzHyGXFMg8GQMMNGDWaLGQaWPlHf8Va96E+rFwypGVVpTIKjo6ORjCflA1A/3xwcDAw+vy/p2JhBdM6Kft1sNiMQCESRj9liRjAQdlcxus7E8Y0mKmZSXu+RsbGY+0DxPHb8qp9R+Tz9Ubt5ErwXhtrq02AwwBrVEIZHLoPYPUlcQigkAZyDI/x7SDzhZBHHK1yxY3EPG43UnATKy4pr/oZ9MxL+y54G/PH9azWPmwiMsVNqPAzozLrhnIcAbGCMFYKafa/lnF8Iv/1hxDQIVx5bbXcax3gcwOMA0N7envb65Z577sHfFvbgR8e7UVOov/NPDjnMF1oqUmtWksPbA9vMRjy2qzkr+04p3Mw5n2CMvQZgP4ALjDETgPcB2KzxkV4AdYrntQD60xhnSvhgex0+2F6XfMMccsghh7cBkq47GWNlYUsejDE7gHsAiDKvewBc4ZxrOdNOAmhhjC1jjFkAPArgmTmPOocccsghB93Q42CsAvAqY+wciLhf4pz/Kvzeo4hx2zDGqhljzwIA5zwI4AsAXgBwGcBPOefZbS2TQw455JBDFHQFY+cb7e3t/M0331zoYeSQQw453DFIFIy9g/Pmcsghhxxy0IMc0eeQQw45LHHkiD6HHHLIYYkjR/Q55JBDDkscOaLPIYcccljiWJRZN4yxYQDpaSDIKAWQWVHt7OJOGm9urNnDnTTeO2mswJ013nTG2sA5V22osCiJPhNgjL2plWq0GHEnjTc31uzhThrvnTRW4M4ab6bHmnPd5JBDDjksceSIPocccshhiWMpE/3jCz2AFHEnjTc31uzhThrvnTRW4M4ab0bHumR99DnkkEMOORCWskWfQw455JADckSfQw455LDksSSInjH2AcbYRcaYxBhrj3nvjxljNxhjVxlj71a8/iHG2Lnw5/52kY/1w4yx8+HxPs8YK12MY2WMORljZxSPEcbY38/HWNMZb/h1C2PsccbYNcbYFcbY+xfxWF8LvybOb/l8jDXd8Sref4YxdiH29cU01vB9dTb8uW+H26cuurEyxhyMsV+Hr9WLjLGv6zpQbN/KO/EBYBWAFQBeA9CueH01gLMArACWAbgJwAigBEA3gLLwdt8H8M5FOlYTgCEApeHt/hbA/1qMY1X5/CkAexfrdRB+7ysA/ir8v0Gc50U61qht5/OR7rUA6kD3BIALi3msAFzhvwzAUwAeXYxjBeAA8I7wNhYABwHcl+w4S8Ki55xf5pxfVXnrIQBPcs59nPNbAG4A2AqgCcA1zvlweLvfAJgXSy6NsbLwI49RB2QX5qEdY5pjjYAx1gKgHHQhzgvSHO8nAXwt/HmJcz4vlZNzObcLgXTGyxjLB/BlAH81fyNNb6yc86nwNiYQgc5LlkqqY+Wcz3LOXw1/1g/gNKhFa0IsCaJPgBoAPYrnveHXbgBYyRhrDPe9fRjRvW0XAqpj5ZwHAHwOwHkQwa8G8C/zP7woaJ1XJT4M4Cc8bHosMFTHK1pkAvhLxthpxth/MMYq5n100Uh2bv817Lb50/DEv9BINN6/BPD/Apid70FpIOG5ZYy9AFo9TwP4z/kdWhyS3mPh6/dBAC8n21lKzcEXEoyx3wCoVHnrTzjnv9D6mMprnHM+zhj7HICfAJAAHAFZ+RlBJsfKGDODiH4jgA4A/wjgj5EhKymTY415/iiA357L2FQPnNnxmkDW0GHO+ZcZY18G8L+RoXFn4dx+lHPexxhzgtwLvw3g3+c+0vCBM3vdbgCwnHP+JcZYY4aGKB80C9ct5/zdjDEbgB8B2AfgpTkPFNkZa9hA/TGAf+CcdyQbwx1D9Jzze9L4WC+iLfVahN0enPNfAvglADDGPgMgNNcxCmR4rBvC+7wJAIyxnwL4ozkOMYJMn1cAYIy1ATBxzk/NcXhxyPB4R0HW5tPh1/8DwKfmNEAFsnDN9oX/TjPGngC5HTJG9Bke7w4AmxljnSCeKWeMvcY5v3uu4wSyc92G9+tljD0Dcp1khOizNNbHAVznnP+9np0tddfNMwAeZYxZGWPLALQAOAEAImOBMVYE4HcBfHfBRknQGmsfgNWMMaFKdy+o0fpCQvO8hvFhxDSNX2CojjfsVvolgLvD270TwKWFGWIEqmNljJlYONsqvMp7AMC8ZbIkgNa5/b+c82rOeSOA3aCY2N0LOE5A+9zmM8aqgIilfADAlQUcJ5CYu/4KQAGA39e9t/mILGf7AeC9oBnQB2AQwAuK9/4EFLG+CkV0GkREl8KPeYmwz2GsnwWR+zkQMZUs1rGG3+sAsPIOuQ4agP+/nTs2ARAGwij8egewEXE4FxLnsbG1EUFcxyIWIoLYSDjfBylDjit+SDjCePR2AOocawUK0hTTAqxAx82kUy71XvY2fDt187a3JTCdetuTbqQ51lqRnnA2YD5W+3SOXyBIUnDRn24k6fcMekkKzqCXpOAMekkKzqCXpOAMekkKzqCXpOB20wo/W2B6XSYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 135 used < 0.7 and 113 used more than 1.6\n"
     ]
    }
   ],
   "source": [
    "\n",
    "minUse = 0.7\n",
    "maxUse = 1.6\n",
    "minNoPlot = 0.9\n",
    "maxNoPlot = 1.1\n",
    "print(\"Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are\",minUse, maxUse)\n",
    "print(\"We do not plot tracts with usage between\",minNoPlot,\"and\",maxNoPlot)\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minNoPlot:\n",
    "        redd = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        gren = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        bluu = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy\n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor=(redd,gren,bluu) )\n",
    "    if tractUse[t] > maxNoPlot:\n",
    "        redd = 1\n",
    "        gren = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        bluu = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy            \n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "MAPboundary = Polygon([Point(-109,37),Point(-102.06,37),Point(-102.06,41),Point(-109,41)])\n",
    "MAPboundary = MAPboundary.buffer(0.01)\n",
    "plotPoly(MAPboundary)\n",
    "plt.show()\n",
    "nWayOverusedTracts = 0\n",
    "nWayUnderusedTracts = 0\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minUse:\n",
    "        nWayUnderusedTracts +=1\n",
    "    if tractUse[t] > maxUse:\n",
    "        nWayOverusedTracts +=1\n",
    "print(\"there are a total of\",nWayUnderusedTracts,\"used <\",minUse,\"and\",nWayOverusedTracts,\"used more than\",maxUse)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b56da923-46e2-42cd-9cfd-b9caa4d4200c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8044167064735105 2.1504951578827765\n"
     ]
    }
   ],
   "source": [
    "maxT = tractUse.index(np.max(tractUse))\n",
    "NLIST = neighborList[maxT]\n",
    "boundaryUse = 0.\n",
    "for t in range(nTracts):\n",
    "    isBoundary = \"no\"\n",
    "    if maxT in HDtractList[t]:\n",
    "        for n in NLIST:\n",
    "            if n not in HDtractList[t]:\n",
    "                isBoundary = \"yes\"\n",
    "    if isBoundary == \"yes\":\n",
    "        plotPoly(tractGeom[t])\n",
    "        frac = 1.\n",
    "        if partialTractNo[t] == maxT:\n",
    "            frac = partialTractUse[t]\n",
    "        boundaryUse += frac * tractPop[t]/avgDistrictPop\n",
    "plt.show()\n",
    "print(boundaryUse, tractUse[maxT])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d731d30f-9dbb-4a26-b789-a5b3f117e284",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getFrac(t,partialNo, partialUse):\n",
    "    frac = 1.\n",
    "    if partialNo == t:\n",
    "        frac = partialUse\n",
    "    result = frac \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8022b71f-696d-4a27-ba5f-14db4f326762",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BELOW CODE IS FOR REDUCING TRACT OVERUSE.  COULD ALSO DEVELOP A CODE FOR INCREASING TRACT USE FOR UNDERUSED TRACTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7ae7837d-2a11-4d4c-9cd4-504d2c748625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block aims to narrow the breadth of the precinct usage distribution\n",
      "the most overused tract 1740 with pop 310 and usage 2.1504951578827765\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 42142.0 OUT and t0 usage now 2.137 1.372\n",
      "all done exchanging OUTs for UUTs for tract 987 . Total popGained = 26249.0 OUT and t0 usage now 2.125 1.372\n",
      "all done exchanging OUTs for UUTs for tract 990 . Total popGained = 27105.0 OUT and t0 usage now 2.114 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2276 . Total popGained = 26115.0 OUT and t0 usage now 2.104 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2263 . Total popGained = 25849.0 OUT and t0 usage now 2.097 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 41896.0 OUT and t0 usage now 2.085 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 42350.0 OUT and t0 usage now 2.075 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 43077.0 OUT and t0 usage now 2.065 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 42415.0 OUT and t0 usage now 2.05 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 43894.0 OUT and t0 usage now 2.038 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 42204.0 OUT and t0 usage now 2.018 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 41711.0 OUT and t0 usage now 1.999 1.372\n",
      "all done exchanging OUTs for UUTs for tract 782 . Total popGained = 40860.0 OUT and t0 usage now 1.975 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 42205.0 OUT and t0 usage now 1.953 1.372\n",
      "all done exchanging OUTs for UUTs for tract 788 . Total popGained = 41842.0 OUT and t0 usage now 1.932 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 37942.0 OUT and t0 usage now 1.914 1.372\n",
      "all done exchanging OUTs for UUTs for tract 89 . Total popGained = 32144.0 OUT and t0 usage now 1.897 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 39830.0 OUT and t0 usage now 1.883 1.372\n",
      "all done exchanging OUTs for UUTs for tract 674 . Total popGained = 38085.0 OUT and t0 usage now 1.868 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2305 . Total popGained = 26407.0 OUT and t0 usage now 1.857 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2325 . Total popGained = 27340.0 OUT and t0 usage now 1.848 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2288 . Total popGained = 35708.0 OUT and t0 usage now 1.838 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2302 . Total popGained = 36465.0 OUT and t0 usage now 1.828 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2347 . Total popGained = 33739.0 OUT and t0 usage now 1.821 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2351 . Total popGained = 36906.0 OUT and t0 usage now 1.812 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2379 . Total popGained = 36719.0 OUT and t0 usage now 1.805 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2369 . Total popGained = 33740.0 OUT and t0 usage now 1.795 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2360 . Total popGained = 26846.0 OUT and t0 usage now 1.789 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2358 . Total popGained = 34671.0 OUT and t0 usage now 1.782 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2333 . Total popGained = 35321.0 OUT and t0 usage now 1.773 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2353 . Total popGained = 33947.0 OUT and t0 usage now 1.765 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2329 . Total popGained = 36012.0 OUT and t0 usage now 1.754 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 34524.0 OUT and t0 usage now 1.747 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2371 . Total popGained = 34105.0 OUT and t0 usage now 1.739 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2298 . Total popGained = 20574.0 OUT and t0 usage now 1.728 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2315 . Total popGained = 34487.0 OUT and t0 usage now 1.718 1.372\n",
      "all done exchanging OUTs for UUTs for tract 68 . Total popGained = 34554.0 OUT and t0 usage now 1.707 1.372\n",
      "all done exchanging OUTs for UUTs for tract 70 . Total popGained = 26758.0 OUT and t0 usage now 1.7 1.372\n",
      "all done exchanging OUTs for UUTs for tract 28 . Total popGained = 35164.0 OUT and t0 usage now 1.686 1.372\n",
      "all done exchanging OUTs for UUTs for tract 661 . Total popGained = 34804.0 OUT and t0 usage now 1.677 1.372\n",
      "all done exchanging OUTs for UUTs for tract 27 . Total popGained = 33386.0 OUT and t0 usage now 1.666 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1694 . Total popGained = 33868.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2308 . Total popGained = 34107.0 OUT and t0 usage now 1.642 1.372\n",
      "all done exchanging OUTs for UUTs for tract 652 . Total popGained = 34672.0 OUT and t0 usage now 1.629 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 35448.0 OUT and t0 usage now 1.616 1.372\n",
      "all done exchanging OUTs for UUTs for tract 818 . Total popGained = 26234.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1687 . Total popGained = 33042.0 OUT and t0 usage now 1.588 1.372\n",
      "all done exchanging OUTs for UUTs for tract 761 . Total popGained = 21625.0 OUT and t0 usage now 1.577 1.372\n",
      "all done exchanging OUTs for UUTs for tract 651 . Total popGained = 34156.0 OUT and t0 usage now 1.563 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1156 . Total popGained = 16374.0 OUT and t0 usage now 1.528 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1096 . Total popGained = 26189.0 OUT and t0 usage now 1.513 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1038 . Total popGained = 23424.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1170 . Total popGained = 23274.0 OUT and t0 usage now 1.49 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1168 . Total popGained = 22868.0 OUT and t0 usage now 1.478 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1175 . Total popGained = 22741.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1176 . Total popGained = 22741.0 OUT and t0 usage now 1.452 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1179 . Total popGained = 23445.0 OUT and t0 usage now 1.437 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1061 . Total popGained = 23874.0 OUT and t0 usage now 1.425 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 29816.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1092 . Total popGained = 25674.0 OUT and t0 usage now 1.4 1.372\n",
      "we have now reduced 1740 usage to 1.3984066408554345 . Time for next OUT\n",
      "the most overused tract 209 with pop 1206 and usage 2.078252623353342\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 15663.0 OUT and t0 usage now 2.067 1.372\n",
      "all done exchanging OUTs for UUTs for tract 987 . Total popGained = 8602.0 OUT and t0 usage now 2.055 1.372\n",
      "all done exchanging OUTs for UUTs for tract 990 . Total popGained = 10670.0 OUT and t0 usage now 2.046 1.372\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 15085.0 OUT and t0 usage now 2.038 1.372\n",
      "all done exchanging OUTs for UUTs for tract 808 . Total popGained = 15392.0 OUT and t0 usage now 2.027 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2273 . Total popGained = 16556.0 OUT and t0 usage now 2.02 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2280 . Total popGained = 11827.0 OUT and t0 usage now 2.01 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2262 . Total popGained = 17220.0 OUT and t0 usage now 1.996 1.372\n",
      "all done exchanging OUTs for UUTs for tract 268 . Total popGained = 6224.0 OUT and t0 usage now 1.984 1.372\n",
      "all done exchanging OUTs for UUTs for tract 812 . Total popGained = 16999.0 OUT and t0 usage now 1.971 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 15263.0 OUT and t0 usage now 1.958 1.372\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 15205.0 OUT and t0 usage now 1.946 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2255 . Total popGained = 17968.0 OUT and t0 usage now 1.933 1.372\n",
      "all done exchanging OUTs for UUTs for tract 801 . Total popGained = 15796.0 OUT and t0 usage now 1.924 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2256 . Total popGained = 15079.0 OUT and t0 usage now 1.903 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2258 . Total popGained = 11735.0 OUT and t0 usage now 1.886 1.372\n",
      "all done exchanging OUTs for UUTs for tract 782 . Total popGained = 16159.0 OUT and t0 usage now 1.862 1.372\n",
      "all done exchanging OUTs for UUTs for tract 793 . Total popGained = 16250.0 OUT and t0 usage now 1.84 1.372\n",
      "all done exchanging OUTs for UUTs for tract 788 . Total popGained = 15084.0 OUT and t0 usage now 1.819 1.372\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 15952.0 OUT and t0 usage now 1.801 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 15904.0 OUT and t0 usage now 1.779 1.372\n",
      "all done exchanging OUTs for UUTs for tract 86 . Total popGained = 14995.0 OUT and t0 usage now 1.767 1.372\n",
      "all done exchanging OUTs for UUTs for tract 958 . Total popGained = 15731.0 OUT and t0 usage now 1.758 1.372\n",
      "all done exchanging OUTs for UUTs for tract 88 . Total popGained = 6279.0 OUT and t0 usage now 1.748 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1933 . Total popGained = 15728.0 OUT and t0 usage now 1.739 1.372\n",
      "all done exchanging OUTs for UUTs for tract 17 . Total popGained = 16587.0 OUT and t0 usage now 1.73 1.372\n",
      "all done exchanging OUTs for UUTs for tract 771 . Total popGained = 15372.0 OUT and t0 usage now 1.72 1.372\n",
      "all done exchanging OUTs for UUTs for tract 657 . Total popGained = 15044.0 OUT and t0 usage now 1.711 1.372\n",
      "all done exchanging OUTs for UUTs for tract 673 . Total popGained = 11868.0 OUT and t0 usage now 1.698 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 16415.0 OUT and t0 usage now 1.686 1.372\n",
      "all done exchanging OUTs for UUTs for tract 653 . Total popGained = 15030.0 OUT and t0 usage now 1.674 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 14822.0 OUT and t0 usage now 1.661 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2321 . Total popGained = 20760.0 OUT and t0 usage now 1.651 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2299 . Total popGained = 16313.0 OUT and t0 usage now 1.642 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2300 . Total popGained = 16643.0 OUT and t0 usage now 1.633 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2340 . Total popGained = 14292.0 OUT and t0 usage now 1.621 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2322 . Total popGained = 8404.0 OUT and t0 usage now 1.612 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2289 . Total popGained = 15365.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2374 . Total popGained = 15877.0 OUT and t0 usage now 1.593 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 11244.0 OUT and t0 usage now 1.585 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 15022.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 17130.0 OUT and t0 usage now 1.568 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2362 . Total popGained = 15786.0 OUT and t0 usage now 1.558 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2335 . Total popGained = 14259.0 OUT and t0 usage now 1.551 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2359 . Total popGained = 14362.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2291 . Total popGained = 12969.0 OUT and t0 usage now 1.534 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 16033.0 OUT and t0 usage now 1.528 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2355 . Total popGained = 16410.0 OUT and t0 usage now 1.517 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 15976.0 OUT and t0 usage now 1.508 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 13897.0 OUT and t0 usage now 1.501 1.372\n",
      "all done exchanging OUTs for UUTs for tract 41 . Total popGained = 22614.0 OUT and t0 usage now 1.486 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2296 . Total popGained = 14807.0 OUT and t0 usage now 1.475 1.372\n",
      "all done exchanging OUTs for UUTs for tract 672 . Total popGained = 24900.0 OUT and t0 usage now 1.465 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2310 . Total popGained = 12754.0 OUT and t0 usage now 1.454 1.372\n",
      "all done exchanging OUTs for UUTs for tract 772 . Total popGained = 25875.0 OUT and t0 usage now 1.438 1.372\n",
      "all done exchanging OUTs for UUTs for tract 29 . Total popGained = 13661.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1828 . Total popGained = 25151.0 OUT and t0 usage now 1.416 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2308 . Total popGained = 16273.0 OUT and t0 usage now 1.406 1.372\n",
      "we have now reduced 209 usage to 1.3964224760699968 . Time for next OUT\n",
      "the most overused tract 285 with pop 2053 and usage 1.997268645382943\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 6436.0 OUT and t0 usage now 1.986 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 6900.0 OUT and t0 usage now 1.977 1.372\n",
      "all done exchanging OUTs for UUTs for tract 668 . Total popGained = 5937.0 OUT and t0 usage now 1.968 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2268 . Total popGained = 1989.0 OUT and t0 usage now 1.96 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 7037.0 OUT and t0 usage now 1.95 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2257 . Total popGained = 1293.0 OUT and t0 usage now 1.938 1.372\n",
      "all done exchanging OUTs for UUTs for tract 669 . Total popGained = 7581.0 OUT and t0 usage now 1.925 1.372\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 5884.0 OUT and t0 usage now 1.912 1.372\n",
      "all done exchanging OUTs for UUTs for tract 801 . Total popGained = 8682.0 OUT and t0 usage now 1.899 1.372\n",
      "all done exchanging OUTs for UUTs for tract 796 . Total popGained = 6177.0 OUT and t0 usage now 1.88 1.372\n",
      "all done exchanging OUTs for UUTs for tract 785 . Total popGained = 6057.0 OUT and t0 usage now 1.857 1.372\n",
      "all done exchanging OUTs for UUTs for tract 784 . Total popGained = 6192.0 OUT and t0 usage now 1.83 1.372\n",
      "all done exchanging OUTs for UUTs for tract 786 . Total popGained = 6149.0 OUT and t0 usage now 1.811 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 6569.0 OUT and t0 usage now 1.787 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 7330.0 OUT and t0 usage now 1.765 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1104 . Total popGained = 7424.0 OUT and t0 usage now 1.755 1.372\n",
      "all done exchanging OUTs for UUTs for tract 953 . Total popGained = 6151.0 OUT and t0 usage now 1.743 1.372\n",
      "all done exchanging OUTs for UUTs for tract 25 . Total popGained = 5225.0 OUT and t0 usage now 1.735 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1691 . Total popGained = 7037.0 OUT and t0 usage now 1.727 1.372\n",
      "all done exchanging OUTs for UUTs for tract 661 . Total popGained = 8291.0 OUT and t0 usage now 1.716 1.372\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 6164.0 OUT and t0 usage now 1.703 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 6910.0 OUT and t0 usage now 1.694 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1684 . Total popGained = 7388.0 OUT and t0 usage now 1.681 1.372\n",
      "all done exchanging OUTs for UUTs for tract 650 . Total popGained = 7316.0 OUT and t0 usage now 1.669 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1685 . Total popGained = 6701.0 OUT and t0 usage now 1.659 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1690 . Total popGained = 5448.0 OUT and t0 usage now 1.646 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1686 . Total popGained = 7165.0 OUT and t0 usage now 1.634 1.372\n",
      "all done exchanging OUTs for UUTs for tract 100 . Total popGained = 7052.0 OUT and t0 usage now 1.625 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2346 . Total popGained = 8563.0 OUT and t0 usage now 1.615 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2324 . Total popGained = 4688.0 OUT and t0 usage now 1.606 1.372\n",
      "all done exchanging OUTs for UUTs for tract 101 . Total popGained = 6638.0 OUT and t0 usage now 1.598 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1689 . Total popGained = 4791.0 OUT and t0 usage now 1.589 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2383 . Total popGained = 2041.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 5667.0 OUT and t0 usage now 1.572 1.372\n",
      "all done exchanging OUTs for UUTs for tract 672 . Total popGained = 6815.0 OUT and t0 usage now 1.564 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2362 . Total popGained = 4683.0 OUT and t0 usage now 1.554 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2363 . Total popGained = 5482.0 OUT and t0 usage now 1.548 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2333 . Total popGained = 5195.0 OUT and t0 usage now 1.54 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2345 . Total popGained = 7016.0 OUT and t0 usage now 1.53 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2329 . Total popGained = 5235.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 5480.0 OUT and t0 usage now 1.513 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 1560.0 OUT and t0 usage now 1.507 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2301 . Total popGained = 5074.0 OUT and t0 usage now 1.493 1.372\n",
      "all done exchanging OUTs for UUTs for tract 772 . Total popGained = 6676.0 OUT and t0 usage now 1.482 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2294 . Total popGained = 4860.0 OUT and t0 usage now 1.468 1.372\n",
      "all done exchanging OUTs for UUTs for tract 30 . Total popGained = 4992.0 OUT and t0 usage now 1.456 1.372\n",
      "all done exchanging OUTs for UUTs for tract 29 . Total popGained = 1748.0 OUT and t0 usage now 1.446 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1700 . Total popGained = 17040.0 OUT and t0 usage now 1.435 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 6722.0 OUT and t0 usage now 1.425 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1832 . Total popGained = 16815.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 285 usage to 1.3992961895930434 . Time for next OUT\n",
      "the most overused tract 1749 with pop 3232 and usage 1.9942532692859403\n",
      "all done exchanging OUTs for UUTs for tract 268 . Total popGained = 12159.0 OUT and t0 usage now 1.983 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2041 . Total popGained = 14719.0 OUT and t0 usage now 1.971 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2260 . Total popGained = 15343.0 OUT and t0 usage now 1.967 1.372\n",
      "all done exchanging OUTs for UUTs for tract 808 . Total popGained = 15223.0 OUT and t0 usage now 1.955 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2300 . Total popGained = 15416.0 OUT and t0 usage now 1.95 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2318 . Total popGained = 16025.0 OUT and t0 usage now 1.944 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2386 . Total popGained = 16898.0 OUT and t0 usage now 1.935 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2374 . Total popGained = 17727.0 OUT and t0 usage now 1.926 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 15077.0 OUT and t0 usage now 1.918 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 14792.0 OUT and t0 usage now 1.91 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2330 . Total popGained = 15285.0 OUT and t0 usage now 1.901 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2368 . Total popGained = 21289.0 OUT and t0 usage now 1.893 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2360 . Total popGained = 14234.0 OUT and t0 usage now 1.884 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2359 . Total popGained = 12743.0 OUT and t0 usage now 1.876 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2367 . Total popGained = 15954.0 OUT and t0 usage now 1.866 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 12886.0 OUT and t0 usage now 1.859 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2355 . Total popGained = 15093.0 OUT and t0 usage now 1.849 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2293 . Total popGained = 14405.0 OUT and t0 usage now 1.841 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 13133.0 OUT and t0 usage now 1.831 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 16227.0 OUT and t0 usage now 1.817 1.372\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 15421.0 OUT and t0 usage now 1.806 1.372\n",
      "all done exchanging OUTs for UUTs for tract 802 . Total popGained = 16628.0 OUT and t0 usage now 1.797 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2294 . Total popGained = 13982.0 OUT and t0 usage now 1.786 1.372\n",
      "all done exchanging OUTs for UUTs for tract 773 . Total popGained = 14439.0 OUT and t0 usage now 1.769 1.372\n",
      "all done exchanging OUTs for UUTs for tract 807 . Total popGained = 15182.0 OUT and t0 usage now 1.756 1.372\n",
      "all done exchanging OUTs for UUTs for tract 796 . Total popGained = 14573.0 OUT and t0 usage now 1.744 1.372\n",
      "all done exchanging OUTs for UUTs for tract 814 . Total popGained = 15601.0 OUT and t0 usage now 1.719 1.372\n",
      "all done exchanging OUTs for UUTs for tract 781 . Total popGained = 14863.0 OUT and t0 usage now 1.701 1.372\n",
      "all done exchanging OUTs for UUTs for tract 955 . Total popGained = 14535.0 OUT and t0 usage now 1.678 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 790 . Total popGained = 15191.0 OUT and t0 usage now 1.655 1.372\n",
      "all done exchanging OUTs for UUTs for tract 956 . Total popGained = 14767.0 OUT and t0 usage now 1.639 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 14363.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 14551.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 854 . Total popGained = 23152.0 OUT and t0 usage now 1.592 1.372\n",
      "all done exchanging OUTs for UUTs for tract 765 . Total popGained = 14112.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 661 . Total popGained = 8787.0 OUT and t0 usage now 1.57 1.372\n",
      "all done exchanging OUTs for UUTs for tract 763 . Total popGained = 21549.0 OUT and t0 usage now 1.561 1.372\n",
      "all done exchanging OUTs for UUTs for tract 817 . Total popGained = 18268.0 OUT and t0 usage now 1.546 1.372\n",
      "all done exchanging OUTs for UUTs for tract 659 . Total popGained = 14902.0 OUT and t0 usage now 1.531 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 14595.0 OUT and t0 usage now 1.517 1.372\n",
      "all done exchanging OUTs for UUTs for tract 666 . Total popGained = 14671.0 OUT and t0 usage now 1.503 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2784 . Total popGained = 17789.0 OUT and t0 usage now 1.489 1.372\n",
      "all done exchanging OUTs for UUTs for tract 653 . Total popGained = 14481.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 13434.0 OUT and t0 usage now 1.459 1.372\n",
      "all done exchanging OUTs for UUTs for tract 737 . Total popGained = 16520.0 OUT and t0 usage now 1.445 1.372\n",
      "all done exchanging OUTs for UUTs for tract 752 . Total popGained = 14806.0 OUT and t0 usage now 1.436 1.372\n",
      "all done exchanging OUTs for UUTs for tract 748 . Total popGained = 16133.0 OUT and t0 usage now 1.417 1.372\n",
      "all done exchanging OUTs for UUTs for tract 738 . Total popGained = 16531.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 1749 usage to 1.3994804730542583 . Time for next OUT\n",
      "the most overused tract 296 with pop 1066 and usage 1.990817002712639\n",
      "all done exchanging OUTs for UUTs for tract 2039 . Total popGained = 12603.0 OUT and t0 usage now 1.979 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 12486.0 OUT and t0 usage now 1.971 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2260 . Total popGained = 5559.0 OUT and t0 usage now 1.964 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 13459.0 OUT and t0 usage now 1.953 1.372\n",
      "all done exchanging OUTs for UUTs for tract 670 . Total popGained = 12741.0 OUT and t0 usage now 1.947 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 14194.0 OUT and t0 usage now 1.93 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 13022.0 OUT and t0 usage now 1.918 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 13826.0 OUT and t0 usage now 1.898 1.372\n",
      "all done exchanging OUTs for UUTs for tract 813 . Total popGained = 13105.0 OUT and t0 usage now 1.884 1.372\n",
      "all done exchanging OUTs for UUTs for tract 814 . Total popGained = 12452.0 OUT and t0 usage now 1.86 1.372\n",
      "all done exchanging OUTs for UUTs for tract 806 . Total popGained = 13511.0 OUT and t0 usage now 1.836 1.372\n",
      "all done exchanging OUTs for UUTs for tract 89 . Total popGained = 9350.0 OUT and t0 usage now 1.821 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 14110.0 OUT and t0 usage now 1.797 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 12808.0 OUT and t0 usage now 1.772 1.372\n",
      "all done exchanging OUTs for UUTs for tract 957 . Total popGained = 12506.0 OUT and t0 usage now 1.756 1.372\n",
      "all done exchanging OUTs for UUTs for tract 953 . Total popGained = 13699.0 OUT and t0 usage now 1.746 1.372\n",
      "all done exchanging OUTs for UUTs for tract 663 . Total popGained = 14109.0 OUT and t0 usage now 1.737 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1692 . Total popGained = 12463.0 OUT and t0 usage now 1.726 1.372\n",
      "all done exchanging OUTs for UUTs for tract 67 . Total popGained = 9110.0 OUT and t0 usage now 1.716 1.372\n",
      "all done exchanging OUTs for UUTs for tract 649 . Total popGained = 13240.0 OUT and t0 usage now 1.705 1.372\n",
      "all done exchanging OUTs for UUTs for tract 653 . Total popGained = 12467.0 OUT and t0 usage now 1.692 1.372\n",
      "all done exchanging OUTs for UUTs for tract 20 . Total popGained = 9017.0 OUT and t0 usage now 1.681 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1683 . Total popGained = 17423.0 OUT and t0 usage now 1.671 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2346 . Total popGained = 4614.0 OUT and t0 usage now 1.658 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2377 . Total popGained = 0.0 OUT and t0 usage now 1.649 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 0.0 OUT and t0 usage now 1.641 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2352 . Total popGained = 5206.0 OUT and t0 usage now 1.632 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2368 . Total popGained = 7544.0 OUT and t0 usage now 1.624 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2353 . Total popGained = 0.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1839 . Total popGained = 18694.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 672 . Total popGained = 21712.0 OUT and t0 usage now 1.592 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2308 . Total popGained = 0.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1699 . Total popGained = 24901.0 OUT and t0 usage now 1.565 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 16767.0 OUT and t0 usage now 1.553 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1700 . Total popGained = 25348.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1828 . Total popGained = 24955.0 OUT and t0 usage now 1.531 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2656 . Total popGained = 16269.0 OUT and t0 usage now 1.497 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2907 . Total popGained = 21687.0 OUT and t0 usage now 1.48 1.372\n",
      "all done exchanging OUTs for UUTs for tract 244 . Total popGained = 27963.0 OUT and t0 usage now 1.466 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2830 . Total popGained = 11265.0 OUT and t0 usage now 1.45 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1833 . Total popGained = 25926.0 OUT and t0 usage now 1.441 1.372\n",
      "all done exchanging OUTs for UUTs for tract 246 . Total popGained = 25790.0 OUT and t0 usage now 1.426 1.372\n",
      "all done exchanging OUTs for UUTs for tract 818 . Total popGained = 11483.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1239 . Total popGained = 9098.0 OUT and t0 usage now 1.402 1.372\n",
      "we have now reduced 296 usage to 1.3932176065527315 . Time for next OUT\n",
      "the most overused tract 743 with pop 769 and usage 1.9684506640529535\n",
      "all done exchanging OUTs for UUTs for tract 988 . Total popGained = 44167.0 OUT and t0 usage now 1.961 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 17706.0 OUT and t0 usage now 1.955 1.372\n",
      "all done exchanging OUTs for UUTs for tract 982 . Total popGained = 18694.0 OUT and t0 usage now 1.941 1.372\n",
      "all done exchanging OUTs for UUTs for tract 812 . Total popGained = 16370.0 OUT and t0 usage now 1.924 1.372\n",
      "all done exchanging OUTs for UUTs for tract 804 . Total popGained = 16488.0 OUT and t0 usage now 1.909 1.372\n",
      "all done exchanging OUTs for UUTs for tract 799 . Total popGained = 16748.0 OUT and t0 usage now 1.9 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 16880.0 OUT and t0 usage now 1.889 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 15872.0 OUT and t0 usage now 1.871 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 35730.0 OUT and t0 usage now 1.845 1.372\n",
      "all done exchanging OUTs for UUTs for tract 92 . Total popGained = 27621.0 OUT and t0 usage now 1.825 1.372\n",
      "all done exchanging OUTs for UUTs for tract 793 . Total popGained = 16210.0 OUT and t0 usage now 1.802 1.372\n",
      "all done exchanging OUTs for UUTs for tract 961 . Total popGained = 17122.0 OUT and t0 usage now 1.781 1.372\n",
      "all done exchanging OUTs for UUTs for tract 956 . Total popGained = 44762.0 OUT and t0 usage now 1.763 1.372\n",
      "all done exchanging OUTs for UUTs for tract 957 . Total popGained = 16062.0 OUT and t0 usage now 1.748 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 27948.0 OUT and t0 usage now 1.737 1.372\n",
      "all done exchanging OUTs for UUTs for tract 93 . Total popGained = 25103.0 OUT and t0 usage now 1.729 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2274 . Total popGained = 42571.0 OUT and t0 usage now 1.721 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2267 . Total popGained = 42523.0 OUT and t0 usage now 1.708 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2280 . Total popGained = 42584.0 OUT and t0 usage now 1.694 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2278 . Total popGained = 42266.0 OUT and t0 usage now 1.682 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2269 . Total popGained = 43531.0 OUT and t0 usage now 1.673 1.372\n",
      "all done exchanging OUTs for UUTs for tract 86 . Total popGained = 21814.0 OUT and t0 usage now 1.663 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2261 . Total popGained = 36735.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1694 . Total popGained = 15530.0 OUT and t0 usage now 1.637 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2255 . Total popGained = 40006.0 OUT and t0 usage now 1.623 1.372\n",
      "all done exchanging OUTs for UUTs for tract 89 . Total popGained = 20740.0 OUT and t0 usage now 1.611 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2258 . Total popGained = 40033.0 OUT and t0 usage now 1.598 1.372\n",
      "all done exchanging OUTs for UUTs for tract 88 . Total popGained = 40519.0 OUT and t0 usage now 1.581 1.372\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 31568.0 OUT and t0 usage now 1.571 1.372\n",
      "all done exchanging OUTs for UUTs for tract 271 . Total popGained = 34596.0 OUT and t0 usage now 1.561 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 16582.0 OUT and t0 usage now 1.551 1.372\n",
      "all done exchanging OUTs for UUTs for tract 649 . Total popGained = 19005.0 OUT and t0 usage now 1.539 1.372\n",
      "all done exchanging OUTs for UUTs for tract 655 . Total popGained = 35709.0 OUT and t0 usage now 1.53 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1832 . Total popGained = 19379.0 OUT and t0 usage now 1.514 1.372\n",
      "all done exchanging OUTs for UUTs for tract 80 . Total popGained = 38860.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1700 . Total popGained = 19816.0 OUT and t0 usage now 1.493 1.372\n",
      "all done exchanging OUTs for UUTs for tract 79 . Total popGained = 34624.0 OUT and t0 usage now 1.482 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1820 . Total popGained = 21307.0 OUT and t0 usage now 1.469 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1818 . Total popGained = 22566.0 OUT and t0 usage now 1.456 1.372\n",
      "all done exchanging OUTs for UUTs for tract 252 . Total popGained = 41527.0 OUT and t0 usage now 1.445 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1722 . Total popGained = 20606.0 OUT and t0 usage now 1.432 1.372\n",
      "all done exchanging OUTs for UUTs for tract 278 . Total popGained = 23465.0 OUT and t0 usage now 1.42 1.372\n",
      "all done exchanging OUTs for UUTs for tract 3086 . Total popGained = 34787.0 OUT and t0 usage now 1.415 1.372\n",
      "all done exchanging OUTs for UUTs for tract 159 . Total popGained = 30895.0 OUT and t0 usage now 1.408 1.372\n",
      "we have now reduced 743 usage to 1.3989463830905147 . Time for next OUT\n",
      "the most overused tract 292 with pop 1513 and usage 1.9564613002999478\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 1140.0 OUT and t0 usage now 1.945 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 1254.0 OUT and t0 usage now 1.936 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 2205.0 OUT and t0 usage now 1.927 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2260 . Total popGained = 1485.0 OUT and t0 usage now 1.918 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 1931.0 OUT and t0 usage now 1.911 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 1084.0 OUT and t0 usage now 1.896 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 3896.0 OUT and t0 usage now 1.884 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 1207.0 OUT and t0 usage now 1.864 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 2253.0 OUT and t0 usage now 1.844 1.372\n",
      "all done exchanging OUTs for UUTs for tract 782 . Total popGained = 1611.0 OUT and t0 usage now 1.821 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 1870.0 OUT and t0 usage now 1.799 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 788 . Total popGained = 1115.0 OUT and t0 usage now 1.778 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 1017.0 OUT and t0 usage now 1.76 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 2767.0 OUT and t0 usage now 1.738 1.372\n",
      "all done exchanging OUTs for UUTs for tract 674 . Total popGained = 2154.0 OUT and t0 usage now 1.725 1.372\n",
      "all done exchanging OUTs for UUTs for tract 662 . Total popGained = 1701.0 OUT and t0 usage now 1.713 1.372\n",
      "all done exchanging OUTs for UUTs for tract 675 . Total popGained = 1568.0 OUT and t0 usage now 1.705 1.372\n",
      "all done exchanging OUTs for UUTs for tract 7 . Total popGained = 2367.0 OUT and t0 usage now 1.698 1.372\n",
      "all done exchanging OUTs for UUTs for tract 648 . Total popGained = 1879.0 OUT and t0 usage now 1.685 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 1015.0 OUT and t0 usage now 1.675 1.372\n",
      "all done exchanging OUTs for UUTs for tract 652 . Total popGained = 1255.0 OUT and t0 usage now 1.662 1.372\n",
      "all done exchanging OUTs for UUTs for tract 5 . Total popGained = 2367.0 OUT and t0 usage now 1.652 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1683 . Total popGained = 1443.0 OUT and t0 usage now 1.641 1.372\n",
      "all done exchanging OUTs for UUTs for tract 92 . Total popGained = 2345.0 OUT and t0 usage now 1.628 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2321 . Total popGained = 2355.0 OUT and t0 usage now 1.616 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1686 . Total popGained = 3474.0 OUT and t0 usage now 1.607 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2386 . Total popGained = 3123.0 OUT and t0 usage now 1.595 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2288 . Total popGained = 2814.0 OUT and t0 usage now 1.587 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2303 . Total popGained = 2770.0 OUT and t0 usage now 1.577 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2348 . Total popGained = 2207.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2352 . Total popGained = 2292.0 OUT and t0 usage now 1.562 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2368 . Total popGained = 3755.0 OUT and t0 usage now 1.554 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 3118.0 OUT and t0 usage now 1.547 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2349 . Total popGained = 2184.0 OUT and t0 usage now 1.536 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1698 . Total popGained = 1287.0 OUT and t0 usage now 1.523 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1834 . Total popGained = 2098.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 98 . Total popGained = 2108.0 OUT and t0 usage now 1.5 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1837 . Total popGained = 1752.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1830 . Total popGained = 1976.0 OUT and t0 usage now 1.476 1.372\n",
      "all done exchanging OUTs for UUTs for tract 40 . Total popGained = 995.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1833 . Total popGained = 4372.0 OUT and t0 usage now 1.45 1.372\n",
      "all done exchanging OUTs for UUTs for tract 39 . Total popGained = 11064.0 OUT and t0 usage now 1.42 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2860 . Total popGained = 14002.0 OUT and t0 usage now 1.402 1.372\n",
      "we have now reduced 292 usage to 1.3984329670641824 . Time for next OUT\n",
      "the most overused tract 706 with pop 2524 and usage 1.9232660883323751\n",
      "all done exchanging OUTs for UUTs for tract 981 . Total popGained = 9816.0 OUT and t0 usage now 1.916 1.372\n",
      "all done exchanging OUTs for UUTs for tract 270 . Total popGained = 8585.0 OUT and t0 usage now 1.906 1.372\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 7566.0 OUT and t0 usage now 1.895 1.372\n",
      "all done exchanging OUTs for UUTs for tract 808 . Total popGained = 7158.0 OUT and t0 usage now 1.88 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 7084.0 OUT and t0 usage now 1.866 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 7593.0 OUT and t0 usage now 1.853 1.372\n",
      "all done exchanging OUTs for UUTs for tract 800 . Total popGained = 7240.0 OUT and t0 usage now 1.838 1.372\n",
      "all done exchanging OUTs for UUTs for tract 802 . Total popGained = 7307.0 OUT and t0 usage now 1.827 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2042 . Total popGained = 7085.0 OUT and t0 usage now 1.812 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 8843.0 OUT and t0 usage now 1.799 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 7822.0 OUT and t0 usage now 1.777 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 7323.0 OUT and t0 usage now 1.753 1.372\n",
      "all done exchanging OUTs for UUTs for tract 780 . Total popGained = 7497.0 OUT and t0 usage now 1.735 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 789 . Total popGained = 7370.0 OUT and t0 usage now 1.715 1.372\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 7137.0 OUT and t0 usage now 1.698 1.372\n",
      "all done exchanging OUTs for UUTs for tract 669 . Total popGained = 6821.0 OUT and t0 usage now 1.685 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2276 . Total popGained = 7664.0 OUT and t0 usage now 1.671 1.372\n",
      "all done exchanging OUTs for UUTs for tract 7 . Total popGained = 15449.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2278 . Total popGained = 6846.0 OUT and t0 usage now 1.639 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2277 . Total popGained = 7844.0 OUT and t0 usage now 1.627 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 7808.0 OUT and t0 usage now 1.616 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 7347.0 OUT and t0 usage now 1.605 1.372\n",
      "all done exchanging OUTs for UUTs for tract 93 . Total popGained = 6911.0 OUT and t0 usage now 1.597 1.372\n",
      "all done exchanging OUTs for UUTs for tract 77 . Total popGained = 7696.0 OUT and t0 usage now 1.586 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1693 . Total popGained = 7844.0 OUT and t0 usage now 1.573 1.372\n",
      "all done exchanging OUTs for UUTs for tract 25 . Total popGained = 14331.0 OUT and t0 usage now 1.567 1.372\n",
      "all done exchanging OUTs for UUTs for tract 771 . Total popGained = 5018.0 OUT and t0 usage now 1.557 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1691 . Total popGained = 7455.0 OUT and t0 usage now 1.545 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1158 . Total popGained = 7923.0 OUT and t0 usage now 1.535 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1692 . Total popGained = 6968.0 OUT and t0 usage now 1.525 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1152 . Total popGained = 6051.0 OUT and t0 usage now 1.513 1.372\n",
      "all done exchanging OUTs for UUTs for tract 663 . Total popGained = 8316.0 OUT and t0 usage now 1.501 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1801 . Total popGained = 18682.0 OUT and t0 usage now 1.492 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 12194.0 OUT and t0 usage now 1.481 1.372\n",
      "all done exchanging OUTs for UUTs for tract 69 . Total popGained = 6164.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1798 . Total popGained = 14999.0 OUT and t0 usage now 1.463 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1688 . Total popGained = 5609.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1685 . Total popGained = 7479.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 20 . Total popGained = 16685.0 OUT and t0 usage now 1.426 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 6198.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 650 . Total popGained = 5360.0 OUT and t0 usage now 1.403 1.372\n",
      "we have now reduced 706 usage to 1.3988022252709016 . Time for next OUT\n",
      "the most overused tract 709 with pop 1915 and usage 1.9038324378381053\n",
      "all done exchanging OUTs for UUTs for tract 988 . Total popGained = 4794.0 OUT and t0 usage now 1.895 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 5937.0 OUT and t0 usage now 1.885 1.372\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 4941.0 OUT and t0 usage now 1.876 1.372\n",
      "all done exchanging OUTs for UUTs for tract 984 . Total popGained = 5359.0 OUT and t0 usage now 1.862 1.372\n",
      "all done exchanging OUTs for UUTs for tract 987 . Total popGained = 5313.0 OUT and t0 usage now 1.842 1.372\n",
      "all done exchanging OUTs for UUTs for tract 800 . Total popGained = 7261.0 OUT and t0 usage now 1.831 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 6028.0 OUT and t0 usage now 1.823 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 6136.0 OUT and t0 usage now 1.805 1.372\n",
      "all done exchanging OUTs for UUTs for tract 5 . Total popGained = 5110.0 OUT and t0 usage now 1.786 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 5653.0 OUT and t0 usage now 1.764 1.372\n",
      "all done exchanging OUTs for UUTs for tract 990 . Total popGained = 5783.0 OUT and t0 usage now 1.741 1.372\n",
      "all done exchanging OUTs for UUTs for tract 955 . Total popGained = 5384.0 OUT and t0 usage now 1.722 1.372\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 7586.0 OUT and t0 usage now 1.704 1.372\n",
      "all done exchanging OUTs for UUTs for tract 956 . Total popGained = 5056.0 OUT and t0 usage now 1.686 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 5419.0 OUT and t0 usage now 1.67 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 4974.0 OUT and t0 usage now 1.659 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2274 . Total popGained = 5731.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2275 . Total popGained = 6734.0 OUT and t0 usage now 1.641 1.372\n",
      "all done exchanging OUTs for UUTs for tract 674 . Total popGained = 5447.0 OUT and t0 usage now 1.627 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2279 . Total popGained = 6244.0 OUT and t0 usage now 1.612 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2263 . Total popGained = 5235.0 OUT and t0 usage now 1.603 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1841 . Total popGained = 6040.0 OUT and t0 usage now 1.589 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 5142.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 675 . Total popGained = 4823.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2254 . Total popGained = 5936.0 OUT and t0 usage now 1.558 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1696 . Total popGained = 6835.0 OUT and t0 usage now 1.548 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1687 . Total popGained = 5372.0 OUT and t0 usage now 1.533 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1683 . Total popGained = 6191.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2256 . Total popGained = 5634.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1690 . Total popGained = 5473.0 OUT and t0 usage now 1.501 1.372\n",
      "all done exchanging OUTs for UUTs for tract 657 . Total popGained = 5906.0 OUT and t0 usage now 1.49 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1828 . Total popGained = 11145.0 OUT and t0 usage now 1.475 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1833 . Total popGained = 7033.0 OUT and t0 usage now 1.465 1.372\n",
      "all done exchanging OUTs for UUTs for tract 659 . Total popGained = 5762.0 OUT and t0 usage now 1.453 1.372\n",
      "all done exchanging OUTs for UUTs for tract 654 . Total popGained = 4980.0 OUT and t0 usage now 1.444 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1832 . Total popGained = 8665.0 OUT and t0 usage now 1.429 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1829 . Total popGained = 7640.0 OUT and t0 usage now 1.417 1.372\n",
      "all done exchanging OUTs for UUTs for tract 655 . Total popGained = 7521.0 OUT and t0 usage now 1.409 1.372\n",
      "all done exchanging OUTs for UUTs for tract 46 . Total popGained = 11143.0 OUT and t0 usage now 1.399 1.372\n",
      "we have now reduced 709 usage to 1.399311431082315 . Time for next OUT\n",
      "the most overused tract 1726 with pop 1749 and usage 1.8476897193037272\n",
      "all done exchanging OUTs for UUTs for tract 2040 . Total popGained = 24202.0 OUT and t0 usage now 1.836 1.372\n",
      "all done exchanging OUTs for UUTs for tract 982 . Total popGained = 24881.0 OUT and t0 usage now 1.822 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1802 . Total popGained = 18673.0 OUT and t0 usage now 1.816 1.372\n",
      "all done exchanging OUTs for UUTs for tract 671 . Total popGained = 24601.0 OUT and t0 usage now 1.806 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2392 . Total popGained = 18080.0 OUT and t0 usage now 1.799 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2387 . Total popGained = 24683.0 OUT and t0 usage now 1.79 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2377 . Total popGained = 25259.0 OUT and t0 usage now 1.782 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2351 . Total popGained = 24020.0 OUT and t0 usage now 1.775 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2286 . Total popGained = 25781.0 OUT and t0 usage now 1.764 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 25727.0 OUT and t0 usage now 1.755 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2363 . Total popGained = 25731.0 OUT and t0 usage now 1.749 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2345 . Total popGained = 24856.0 OUT and t0 usage now 1.742 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2358 . Total popGained = 24817.0 OUT and t0 usage now 1.733 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2354 . Total popGained = 24380.0 OUT and t0 usage now 1.725 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 26505.0 OUT and t0 usage now 1.716 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2329 . Total popGained = 26258.0 OUT and t0 usage now 1.705 1.372\n",
      "all done exchanging OUTs for UUTs for tract 22 . Total popGained = 32776.0 OUT and t0 usage now 1.7 1.372\n",
      "all done exchanging OUTs for UUTs for tract 668 . Total popGained = 24350.0 OUT and t0 usage now 1.69 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2295 . Total popGained = 18045.0 OUT and t0 usage now 1.679 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2310 . Total popGained = 17854.0 OUT and t0 usage now 1.668 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2311 . Total popGained = 23076.0 OUT and t0 usage now 1.657 1.372\n",
      "all done exchanging OUTs for UUTs for tract 102 . Total popGained = 25835.0 OUT and t0 usage now 1.649 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 24813.0 OUT and t0 usage now 1.637 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2736 . Total popGained = 5400.0 OUT and t0 usage now 1.625 1.372\n",
      "all done exchanging OUTs for UUTs for tract 892 . Total popGained = 26349.0 OUT and t0 usage now 1.609 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 24925.0 OUT and t0 usage now 1.596 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1073 . Total popGained = 27183.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 765 . Total popGained = 26409.0 OUT and t0 usage now 1.568 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1169 . Total popGained = 26684.0 OUT and t0 usage now 1.559 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1051 . Total popGained = 32439.0 OUT and t0 usage now 1.546 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1173 . Total popGained = 16917.0 OUT and t0 usage now 1.532 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2833 . Total popGained = 10147.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2784 . Total popGained = 10758.0 OUT and t0 usage now 1.5 1.372\n",
      "all done exchanging OUTs for UUTs for tract 744 . Total popGained = 34227.0 OUT and t0 usage now 1.481 1.372\n",
      "all done exchanging OUTs for UUTs for tract 88 . Total popGained = 24496.0 OUT and t0 usage now 1.468 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1052 . Total popGained = 31756.0 OUT and t0 usage now 1.454 1.372\n",
      "all done exchanging OUTs for UUTs for tract 763 . Total popGained = 22129.0 OUT and t0 usage now 1.441 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2822 . Total popGained = 9641.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 17489.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1157 . Total popGained = 23428.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 1726 usage to 1.398002048594716 . Time for next OUT\n",
      "the most overused tract 216 with pop 758 and usage 1.8043262967303195\n",
      "all done exchanging OUTs for UUTs for tract 2039 . Total popGained = 8666.0 OUT and t0 usage now 1.793 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2042 . Total popGained = 9039.0 OUT and t0 usage now 1.788 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 10098.0 OUT and t0 usage now 1.778 1.372\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 8865.0 OUT and t0 usage now 1.769 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 9378.0 OUT and t0 usage now 1.752 1.372\n",
      "all done exchanging OUTs for UUTs for tract 102 . Total popGained = 8324.0 OUT and t0 usage now 1.743 1.372\n",
      "all done exchanging OUTs for UUTs for tract 797 . Total popGained = 8758.0 OUT and t0 usage now 1.728 1.372\n",
      "all done exchanging OUTs for UUTs for tract 795 . Total popGained = 8986.0 OUT and t0 usage now 1.712 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 7719.0 OUT and t0 usage now 1.689 1.372\n",
      "all done exchanging OUTs for UUTs for tract 793 . Total popGained = 8827.0 OUT and t0 usage now 1.673 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 8439.0 OUT and t0 usage now 1.648 1.372\n",
      "all done exchanging OUTs for UUTs for tract 8 . Total popGained = 10988.0 OUT and t0 usage now 1.628 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 8101.0 OUT and t0 usage now 1.611 1.372\n",
      "all done exchanging OUTs for UUTs for tract 957 . Total popGained = 8165.0 OUT and t0 usage now 1.587 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1933 . Total popGained = 9794.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 7848.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1692 . Total popGained = 8684.0 OUT and t0 usage now 1.555 1.372\n",
      "all done exchanging OUTs for UUTs for tract 652 . Total popGained = 8713.0 OUT and t0 usage now 1.542 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 8061.0 OUT and t0 usage now 1.53 1.372\n",
      "all done exchanging OUTs for UUTs for tract 651 . Total popGained = 8879.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1840 . Total popGained = 10579.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1841 . Total popGained = 9323.0 OUT and t0 usage now 1.498 1.372\n",
      "all done exchanging OUTs for UUTs for tract 772 . Total popGained = 8071.0 OUT and t0 usage now 1.488 1.372\n",
      "all done exchanging OUTs for UUTs for tract 92 . Total popGained = 9822.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 252 . Total popGained = 7520.0 OUT and t0 usage now 1.46 1.372\n",
      "all done exchanging OUTs for UUTs for tract 251 . Total popGained = 8291.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 8624.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 93 . Total popGained = 8294.0 OUT and t0 usage now 1.43 1.372\n",
      "all done exchanging OUTs for UUTs for tract 766 . Total popGained = 5093.0 OUT and t0 usage now 1.418 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2778 . Total popGained = 3693.0 OUT and t0 usage now 1.404 1.372\n",
      "we have now reduced 216 usage to 1.3958585409668716 . Time for next OUT\n",
      "the most overused tract 227 with pop 318 and usage 1.6954937497770062\n",
      "all done exchanging OUTs for UUTs for tract 137 . Total popGained = 11431.0 OUT and t0 usage now 1.69 1.372\n",
      "all done exchanging OUTs for UUTs for tract 140 . Total popGained = 17220.0 OUT and t0 usage now 1.687 1.372\n",
      "all done exchanging OUTs for UUTs for tract 695 . Total popGained = 16049.0 OUT and t0 usage now 1.683 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1040 . Total popGained = 9354.0 OUT and t0 usage now 1.672 1.372\n",
      "all done exchanging OUTs for UUTs for tract 56 . Total popGained = 16199.0 OUT and t0 usage now 1.663 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1237 . Total popGained = 8509.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 690 . Total popGained = 17755.0 OUT and t0 usage now 1.642 1.372\n",
      "all done exchanging OUTs for UUTs for tract 694 . Total popGained = 19438.0 OUT and t0 usage now 1.636 1.372\n",
      "all done exchanging OUTs for UUTs for tract 922 . Total popGained = 10626.0 OUT and t0 usage now 1.62 1.372\n",
      "all done exchanging OUTs for UUTs for tract 699 . Total popGained = 16583.0 OUT and t0 usage now 1.608 1.372\n",
      "all done exchanging OUTs for UUTs for tract 734 . Total popGained = 18317.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 4576.0 OUT and t0 usage now 1.593 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2310 . Total popGained = 7420.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 840 . Total popGained = 16375.0 OUT and t0 usage now 1.562 1.372\n",
      "all done exchanging OUTs for UUTs for tract 28 . Total popGained = 8261.0 OUT and t0 usage now 1.554 1.372\n",
      "all done exchanging OUTs for UUTs for tract 902 . Total popGained = 8208.0 OUT and t0 usage now 1.543 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2307 . Total popGained = 5720.0 OUT and t0 usage now 1.532 1.372\n",
      "all done exchanging OUTs for UUTs for tract 881 . Total popGained = 10385.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 869 . Total popGained = 11536.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2390 . Total popGained = 3735.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2334 . Total popGained = 5239.0 OUT and t0 usage now 1.495 1.372\n",
      "all done exchanging OUTs for UUTs for tract 950 . Total popGained = 10155.0 OUT and t0 usage now 1.485 1.372\n",
      "all done exchanging OUTs for UUTs for tract 872 . Total popGained = 9416.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 844 . Total popGained = 15992.0 OUT and t0 usage now 1.463 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2359 . Total popGained = 4992.0 OUT and t0 usage now 1.456 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2296 . Total popGained = 5687.0 OUT and t0 usage now 1.443 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2364 . Total popGained = 3735.0 OUT and t0 usage now 1.43 1.372\n",
      "all done exchanging OUTs for UUTs for tract 880 . Total popGained = 15244.0 OUT and t0 usage now 1.422 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2376 . Total popGained = 13273.0 OUT and t0 usage now 1.404 1.372\n",
      "we have now reduced 227 usage to 1.398906838821596 . Time for next OUT\n",
      "the most overused tract 181 with pop 1389 and usage 1.6926047947646872\n",
      "all done exchanging OUTs for UUTs for tract 279 . Total popGained = 7104.0 OUT and t0 usage now 1.688 1.372\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 6934.0 OUT and t0 usage now 1.677 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2040 . Total popGained = 4327.0 OUT and t0 usage now 1.669 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 4789.0 OUT and t0 usage now 1.66 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1798 . Total popGained = 4056.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2041 . Total popGained = 3728.0 OUT and t0 usage now 1.639 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2326 . Total popGained = 5206.0 OUT and t0 usage now 1.629 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1805 . Total popGained = 6066.0 OUT and t0 usage now 1.622 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2387 . Total popGained = 8521.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 103 . Total popGained = 5469.0 OUT and t0 usage now 1.608 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 5721.0 OUT and t0 usage now 1.601 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2270 . Total popGained = 4988.0 OUT and t0 usage now 1.592 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2357 . Total popGained = 5552.0 OUT and t0 usage now 1.582 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2363 . Total popGained = 8187.0 OUT and t0 usage now 1.573 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2379 . Total popGained = 6814.0 OUT and t0 usage now 1.565 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2330 . Total popGained = 5206.0 OUT and t0 usage now 1.557 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2349 . Total popGained = 5372.0 OUT and t0 usage now 1.549 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2338 . Total popGained = 6233.0 OUT and t0 usage now 1.539 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 5832.0 OUT and t0 usage now 1.528 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2307 . Total popGained = 5866.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 6337.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2388 . Total popGained = 6602.0 OUT and t0 usage now 1.503 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2371 . Total popGained = 7324.0 OUT and t0 usage now 1.492 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2296 . Total popGained = 5807.0 OUT and t0 usage now 1.484 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2315 . Total popGained = 6895.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2316 . Total popGained = 5347.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1188 . Total popGained = 5420.0 OUT and t0 usage now 1.452 1.372\n",
      "all done exchanging OUTs for UUTs for tract 30 . Total popGained = 5303.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 24 . Total popGained = 6354.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1189 . Total popGained = 5553.0 OUT and t0 usage now 1.416 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1045 . Total popGained = 6003.0 OUT and t0 usage now 1.402 1.372\n",
      "we have now reduced 181 usage to 1.3989456353397476 . Time for next OUT\n",
      "the most overused tract 194 with pop 795 and usage 1.671409425544806\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 14293.0 OUT and t0 usage now 1.66 1.372\n",
      "all done exchanging OUTs for UUTs for tract 671 . Total popGained = 11374.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 9 . Total popGained = 10539.0 OUT and t0 usage now 1.635 1.372\n",
      "all done exchanging OUTs for UUTs for tract 989 . Total popGained = 14100.0 OUT and t0 usage now 1.625 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2281 . Total popGained = 9427.0 OUT and t0 usage now 1.617 1.372\n",
      "all done exchanging OUTs for UUTs for tract 811 . Total popGained = 11063.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2267 . Total popGained = 11318.0 OUT and t0 usage now 1.591 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2275 . Total popGained = 15915.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2266 . Total popGained = 9040.0 OUT and t0 usage now 1.568 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2264 . Total popGained = 13815.0 OUT and t0 usage now 1.552 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2282 . Total popGained = 5193.0 OUT and t0 usage now 1.534 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2263 . Total popGained = 16604.0 OUT and t0 usage now 1.524 1.372\n",
      "all done exchanging OUTs for UUTs for tract 792 . Total popGained = 11244.0 OUT and t0 usage now 1.504 1.372\n",
      "all done exchanging OUTs for UUTs for tract 784 . Total popGained = 11151.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2257 . Total popGained = 14285.0 OUT and t0 usage now 1.46 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2255 . Total popGained = 7929.0 OUT and t0 usage now 1.438 1.372\n",
      "all done exchanging OUTs for UUTs for tract 960 . Total popGained = 14775.0 OUT and t0 usage now 1.42 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2256 . Total popGained = 5298.0 OUT and t0 usage now 1.4 1.372\n",
      "we have now reduced 194 usage to 1.399526197522081 . Time for next OUT\n",
      "the most overused tract 108 with pop 326 and usage 1.671352954254219\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 8587.0 OUT and t0 usage now 1.658 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 8427.0 OUT and t0 usage now 1.651 1.372\n",
      "all done exchanging OUTs for UUTs for tract 981 . Total popGained = 10140.0 OUT and t0 usage now 1.635 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 7568.0 OUT and t0 usage now 1.626 1.372\n",
      "all done exchanging OUTs for UUTs for tract 809 . Total popGained = 10035.0 OUT and t0 usage now 1.615 1.372\n",
      "all done exchanging OUTs for UUTs for tract 807 . Total popGained = 5788.0 OUT and t0 usage now 1.6 1.372\n",
      "all done exchanging OUTs for UUTs for tract 785 . Total popGained = 4812.0 OUT and t0 usage now 1.589 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 4964.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 8445.0 OUT and t0 usage now 1.546 1.372\n",
      "all done exchanging OUTs for UUTs for tract 780 . Total popGained = 7816.0 OUT and t0 usage now 1.521 1.372\n",
      "all done exchanging OUTs for UUTs for tract 955 . Total popGained = 6299.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 960 . Total popGained = 4821.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 4990.0 OUT and t0 usage now 1.462 1.372\n",
      "all done exchanging OUTs for UUTs for tract 669 . Total popGained = 7337.0 OUT and t0 usage now 1.454 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1835 . Total popGained = 1264.0 OUT and t0 usage now 1.437 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1933 . Total popGained = 4391.0 OUT and t0 usage now 1.423 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 7076.0 OUT and t0 usage now 1.406 1.372\n",
      "we have now reduced 108 usage to 1.398091329889257 . Time for next OUT\n",
      "the most overused tract 45 with pop 1200 and usage 1.6647223217132447\n",
      "all done exchanging OUTs for UUTs for tract 2041 . Total popGained = 13609.0 OUT and t0 usage now 1.659 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2042 . Total popGained = 3900.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2387 . Total popGained = 5453.0 OUT and t0 usage now 1.645 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2337 . Total popGained = 4423.0 OUT and t0 usage now 1.637 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 4536.0 OUT and t0 usage now 1.629 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2364 . Total popGained = 5059.0 OUT and t0 usage now 1.621 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2334 . Total popGained = 5914.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2369 . Total popGained = 12498.0 OUT and t0 usage now 1.607 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2367 . Total popGained = 10503.0 OUT and t0 usage now 1.599 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 5446.0 OUT and t0 usage now 1.591 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2349 . Total popGained = 9088.0 OUT and t0 usage now 1.581 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 2349.0 OUT and t0 usage now 1.576 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2295 . Total popGained = 0.0 OUT and t0 usage now 1.565 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 12808.0 OUT and t0 usage now 1.555 1.372\n",
      "all done exchanging OUTs for UUTs for tract 29 . Total popGained = 1821.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 13335.0 OUT and t0 usage now 1.537 1.372\n",
      "all done exchanging OUTs for UUTs for tract 69 . Total popGained = 12626.0 OUT and t0 usage now 1.525 1.372\n",
      "all done exchanging OUTs for UUTs for tract 68 . Total popGained = 11694.0 OUT and t0 usage now 1.515 1.372\n",
      "all done exchanging OUTs for UUTs for tract 662 . Total popGained = 13171.0 OUT and t0 usage now 1.499 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 11575.0 OUT and t0 usage now 1.484 1.372\n",
      "all done exchanging OUTs for UUTs for tract 9 . Total popGained = 14520.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 792 . Total popGained = 13785.0 OUT and t0 usage now 1.45 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 12060.0 OUT and t0 usage now 1.429 1.372\n",
      "all done exchanging OUTs for UUTs for tract 940 . Total popGained = 6742.0 OUT and t0 usage now 1.411 1.372\n",
      "we have now reduced 45 usage to 1.3952135546248492 . Time for next OUT\n",
      "the most overused tract 3099 with pop 349 and usage 1.6240984572495274\n",
      "all done exchanging OUTs for UUTs for tract 191 . Total popGained = 6634.0 OUT and t0 usage now 1.618 1.372\n",
      "all done exchanging OUTs for UUTs for tract 204 . Total popGained = 7321.0 OUT and t0 usage now 1.613 1.372\n",
      "all done exchanging OUTs for UUTs for tract 3105 . Total popGained = 5138.0 OUT and t0 usage now 1.607 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1799 . Total popGained = 7268.0 OUT and t0 usage now 1.603 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1147 . Total popGained = 8375.0 OUT and t0 usage now 1.598 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1171 . Total popGained = 11115.0 OUT and t0 usage now 1.587 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1187 . Total popGained = 11724.0 OUT and t0 usage now 1.575 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1241 . Total popGained = 10461.0 OUT and t0 usage now 1.567 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1169 . Total popGained = 7138.0 OUT and t0 usage now 1.559 1.372\n",
      "all done exchanging OUTs for UUTs for tract 18 . Total popGained = 8708.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 898 . Total popGained = 5804.0 OUT and t0 usage now 1.536 1.372\n",
      "all done exchanging OUTs for UUTs for tract 820 . Total popGained = 11384.0 OUT and t0 usage now 1.519 1.372\n",
      "all done exchanging OUTs for UUTs for tract 908 . Total popGained = 7374.0 OUT and t0 usage now 1.507 1.372\n",
      "all done exchanging OUTs for UUTs for tract 830 . Total popGained = 2095.0 OUT and t0 usage now 1.496 1.372\n",
      "all done exchanging OUTs for UUTs for tract 935 . Total popGained = 9276.0 OUT and t0 usage now 1.482 1.372\n",
      "all done exchanging OUTs for UUTs for tract 872 . Total popGained = 8126.0 OUT and t0 usage now 1.47 1.372\n",
      "all done exchanging OUTs for UUTs for tract 943 . Total popGained = 7261.0 OUT and t0 usage now 1.46 1.372\n",
      "all done exchanging OUTs for UUTs for tract 880 . Total popGained = 8267.0 OUT and t0 usage now 1.446 1.372\n",
      "all done exchanging OUTs for UUTs for tract 882 . Total popGained = 11853.0 OUT and t0 usage now 1.427 1.372\n",
      "all done exchanging OUTs for UUTs for tract 883 . Total popGained = 9714.0 OUT and t0 usage now 1.409 1.372\n",
      "we have now reduced 3099 usage to 1.3975960707440649 . Time for next OUT\n",
      "the most overused tract 184 with pop 3043 and usage 1.60521592403614\n",
      "all done exchanging OUTs for UUTs for tract 279 . Total popGained = 11759.0 OUT and t0 usage now 1.6 1.372\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 9988.0 OUT and t0 usage now 1.59 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1802 . Total popGained = 8764.0 OUT and t0 usage now 1.584 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1798 . Total popGained = 9303.0 OUT and t0 usage now 1.575 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2326 . Total popGained = 10560.0 OUT and t0 usage now 1.564 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2347 . Total popGained = 10404.0 OUT and t0 usage now 1.559 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2386 . Total popGained = 7196.0 OUT and t0 usage now 1.55 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2370 . Total popGained = 6830.0 OUT and t0 usage now 1.543 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 6583.0 OUT and t0 usage now 1.534 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 6886.0 OUT and t0 usage now 1.527 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2375 . Total popGained = 9945.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2345 . Total popGained = 6385.0 OUT and t0 usage now 1.51 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2379 . Total popGained = 5954.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2380 . Total popGained = 10474.0 OUT and t0 usage now 1.494 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2335 . Total popGained = 6669.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2333 . Total popGained = 7251.0 OUT and t0 usage now 1.476 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 8045.0 OUT and t0 usage now 1.467 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2355 . Total popGained = 6605.0 OUT and t0 usage now 1.457 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2285 . Total popGained = 11173.0 OUT and t0 usage now 1.445 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1799 . Total popGained = 9877.0 OUT and t0 usage now 1.442 1.372\n",
      "all done exchanging OUTs for UUTs for tract 984 . Total popGained = 5092.0 OUT and t0 usage now 1.43 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2298 . Total popGained = 7298.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1239 . Total popGained = 7848.0 OUT and t0 usage now 1.406 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2295 . Total popGained = 4464.0 OUT and t0 usage now 1.401 1.372\n",
      "we have now reduced 184 usage to 1.3978734751375617 . Time for next OUT\n",
      "the most overused tract 994 with pop 658 and usage 1.5556752551304076\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 6932.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 8012.0 OUT and t0 usage now 1.536 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 6908.0 OUT and t0 usage now 1.529 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 6535.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 670 . Total popGained = 5848.0 OUT and t0 usage now 1.509 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 8679.0 OUT and t0 usage now 1.495 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 7447.0 OUT and t0 usage now 1.483 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 7102.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 792 . Total popGained = 6225.0 OUT and t0 usage now 1.442 1.372\n",
      "all done exchanging OUTs for UUTs for tract 784 . Total popGained = 7248.0 OUT and t0 usage now 1.416 1.372\n",
      "all done exchanging OUTs for UUTs for tract 86 . Total popGained = 10536.0 OUT and t0 usage now 1.4 1.372\n",
      "we have now reduced 994 usage to 1.3959749305213256 . Time for next OUT\n",
      "the most overused tract 241 with pop 942 and usage 1.5277985712489393\n",
      "all done exchanging OUTs for UUTs for tract 281 . Total popGained = 3435.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 274 . Total popGained = 3435.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 273 . Total popGained = 7997.0 OUT and t0 usage now 1.506 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1801 . Total popGained = 2979.0 OUT and t0 usage now 1.497 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1150 . Total popGained = 8737.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1167 . Total popGained = 5421.0 OUT and t0 usage now 1.476 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1045 . Total popGained = 5851.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1042 . Total popGained = 7704.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1256 . Total popGained = 6737.0 OUT and t0 usage now 1.435 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1209 . Total popGained = 9143.0 OUT and t0 usage now 1.424 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1196 . Total popGained = 7149.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1092 . Total popGained = 9191.0 OUT and t0 usage now 1.403 1.372\n",
      "we have now reduced 241 usage to 1.3996093329181176 . Time for next OUT\n",
      "the most overused tract 1131 with pop 1686 and usage 1.5032930276768115\n",
      "all done exchanging OUTs for UUTs for tract 2039 . Total popGained = 13894.0 OUT and t0 usage now 1.49 1.372\n",
      "all done exchanging OUTs for UUTs for tract 668 . Total popGained = 11319.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 14567.0 OUT and t0 usage now 1.479 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 13966.0 OUT and t0 usage now 1.466 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 12731.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 12426.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 13558.0 OUT and t0 usage now 1.423 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 12644.0 OUT and t0 usage now 1.401 1.372\n",
      "we have now reduced 1131 usage to 1.3967065220064578 . Time for next OUT\n",
      "the most overused tract 2544 with pop 1721 and usage 1.4919600203105936\n",
      "all done exchanging OUTs for UUTs for tract 984 . Total popGained = 7810.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2272 . Total popGained = 10624.0 OUT and t0 usage now 1.467 1.372\n",
      "all done exchanging OUTs for UUTs for tract 9 . Total popGained = 3301.0 OUT and t0 usage now 1.453 1.372\n",
      "all done exchanging OUTs for UUTs for tract 103 . Total popGained = 7922.0 OUT and t0 usage now 1.443 1.372\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 5871.0 OUT and t0 usage now 1.435 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2267 . Total popGained = 9141.0 OUT and t0 usage now 1.423 1.372\n",
      "all done exchanging OUTs for UUTs for tract 804 . Total popGained = 4783.0 OUT and t0 usage now 1.411 1.372\n",
      "we have now reduced 2544 usage to 1.3978174978371911 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.4750269930239013\n",
      "all done exchanging OUTs for UUTs for tract 24 . Total popGained = 4170.0 OUT and t0 usage now 1.47 1.372\n",
      "all done exchanging OUTs for UUTs for tract 26 . Total popGained = 4667.0 OUT and t0 usage now 1.466 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2285 . Total popGained = 9737.0 OUT and t0 usage now 1.457 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2312 . Total popGained = 9038.0 OUT and t0 usage now 1.447 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2330 . Total popGained = 7659.0 OUT and t0 usage now 1.438 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2316 . Total popGained = 7323.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2337 . Total popGained = 6669.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 6608.0 OUT and t0 usage now 1.409 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2382 . Total popGained = 10789.0 OUT and t0 usage now 1.401 1.372\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 247 with pop 284 and usage 1.4576553129097967\n",
      "all done exchanging OUTs for UUTs for tract 276 . Total popGained = 11164.0 OUT and t0 usage now 1.452 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1191 . Total popGained = 4557.0 OUT and t0 usage now 1.44 1.372\n",
      "all done exchanging OUTs for UUTs for tract 275 . Total popGained = 9347.0 OUT and t0 usage now 1.431 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1066 . Total popGained = 13174.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 10755.0 OUT and t0 usage now 1.411 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2762 . Total popGained = 9996.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 247 usage to 1.3991543203078076 . Time for next OUT\n",
      "the most overused tract 3095 with pop 483 and usage 1.4561801987420921\n",
      "all done exchanging OUTs for UUTs for tract 191 . Total popGained = 5162.0 OUT and t0 usage now 1.449 1.367\n",
      "all done exchanging OUTs for UUTs for tract 202 . Total popGained = 7742.0 OUT and t0 usage now 1.444 1.363\n",
      "all done exchanging OUTs for UUTs for tract 131 . Total popGained = 5452.0 OUT and t0 usage now 1.43 1.349\n",
      "all done exchanging OUTs for UUTs for tract 3105 . Total popGained = 4510.0 OUT and t0 usage now 1.424 1.343\n",
      "all done exchanging OUTs for UUTs for tract 1795 . Total popGained = 3509.0 OUT and t0 usage now 1.42 1.339\n",
      "all done exchanging OUTs for UUTs for tract 281 . Total popGained = 5787.0 OUT and t0 usage now 1.415 1.334\n",
      "all done exchanging OUTs for UUTs for tract 1155 . Total popGained = 4153.0 OUT and t0 usage now 1.41 1.329\n",
      "all done exchanging OUTs for UUTs for tract 280 . Total popGained = 4220.0 OUT and t0 usage now 1.403 1.322\n",
      "we have now reduced 3095 usage to 1.398825089015493 . Time for next OUT\n",
      "the most overused tract 1727 with pop 2308 and usage 1.4473808375671393\n",
      "all done exchanging OUTs for UUTs for tract 279 . Total popGained = 17837.0 OUT and t0 usage now 1.442 1.318\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 16988.0 OUT and t0 usage now 1.432 1.318\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 20685.0 OUT and t0 usage now 1.427 1.318\n",
      "all done exchanging OUTs for UUTs for tract 2317 . Total popGained = 23328.0 OUT and t0 usage now 1.42 1.318\n",
      "all done exchanging OUTs for UUTs for tract 2391 . Total popGained = 5107.0 OUT and t0 usage now 1.413 1.318\n",
      "all done exchanging OUTs for UUTs for tract 2381 . Total popGained = 17314.0 OUT and t0 usage now 1.405 1.318\n",
      "we have now reduced 1727 usage to 1.399727628558172 . Time for next OUT\n",
      "the most overused tract 53 with pop 500 and usage 1.4392773871376385\n",
      "all done exchanging OUTs for UUTs for tract 671 . Total popGained = 3080.0 OUT and t0 usage now 1.436 1.318\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 3418.0 OUT and t0 usage now 1.431 1.318\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 3106.0 OUT and t0 usage now 1.42 1.318\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 2692.0 OUT and t0 usage now 1.41 1.318\n",
      "we have now reduced 53 usage to 1.3995746931697697 . Time for next OUT\n",
      "the most overused tract 1732 with pop 4666 and usage 1.4367101940372193\n",
      "all done exchanging OUTs for UUTs for tract 796 . Total popGained = 16769.0 OUT and t0 usage now 1.421 1.318\n",
      "all done exchanging OUTs for UUTs for tract 753 . Total popGained = 17857.0 OUT and t0 usage now 1.395 1.318\n",
      "we have now reduced 1732 usage to 1.3948401323654054 . Time for next OUT\n",
      "the most overused tract 231 with pop 653 and usage 1.4339844335898855\n",
      "all done exchanging OUTs for UUTs for tract 3104 . Total popGained = 0.0 OUT and t0 usage now 1.429 1.318\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 0.0 OUT and t0 usage now 1.426 1.318\n",
      "all done exchanging OUTs for UUTs for tract 882 . Total popGained = 1719.0 OUT and t0 usage now 1.414 1.318\n",
      "all done exchanging OUTs for UUTs for tract 938 . Total popGained = 1748.0 OUT and t0 usage now 1.398 1.318\n",
      "we have now reduced 231 usage to 1.3978953581698006 . Time for next OUT\n",
      "the most overused tract 35 with pop 165 and usage 1.4300770699761023\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 20671.0 OUT and t0 usage now 1.411 1.318\n",
      "we have now reduced 35 usage to 1.3958100453191828 . Time for next OUT\n",
      "the most overused tract 219 with pop 1015 and usage 1.4261391192944435\n",
      "all done exchanging OUTs for UUTs for tract 854 . Total popGained = 10170.0 OUT and t0 usage now 1.411 1.318\n",
      "all done exchanging OUTs for UUTs for tract 240 . Total popGained = 25737.0 OUT and t0 usage now 1.398 1.318\n",
      "we have now reduced 219 usage to 1.3978509152025884 . Time for next OUT\n",
      "the most overused tract 4 with pop 89 and usage 1.4203945675175458\n",
      "all done exchanging OUTs for UUTs for tract 164 . Total popGained = 1523.0 OUT and t0 usage now 1.412 1.318\n",
      "we have now reduced 4 usage to 1.399476316284457 . Time for next OUT\n",
      "the most overused tract 207 with pop 686 and usage 1.41201174841705\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 13994.0 OUT and t0 usage now 1.4 1.318\n",
      "we have now reduced 207 usage to 1.3996218032275238 . Time for next OUT\n",
      "the most overused tract 999 with pop 843 and usage 1.4096664611728118\n",
      "we have now reduced 999 usage to 1.398939223904045 . Time for next OUT\n",
      "the most overused tract 122 with pop 842 and usage 1.40957311013327\n",
      "we have now reduced 122 usage to 1.3992199821466744 . Time for next OUT\n",
      "the most overused tract 52 with pop 1197 and usage 1.4073256832603773\n",
      "we have now reduced 52 usage to 1.3965984459916105 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/2507848697.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtt\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mHDtractList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m                     \u001b[0misOUTboundaryTract\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOUT\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMAPboundary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m                 \u001b[0misOUTboundaryTract\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misOUTboundaryTract\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/geopandas/geoseries.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrapped_pandas_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__getitem__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/geopandas/geoseries.py\u001b[0m in \u001b[0;36m_wrapped_pandas_method\u001b[0;34m(self, mtd, *args, **kwargs)\u001b[0m\n\u001b[1;32m    599\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wrapped_pandas_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmtd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;34m\"\"\"Wrap a generic pandas method to ensure it returns a GeoSeries\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m         \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmtd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGeoSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpack_1tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_fallback_to_positional\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"This code block aims to narrow the breadth of the precinct usage distribution\")  #based on OH_patch_legisl_org patching block\n",
    "# GENERAL SCHEME - identify the most overused tract (OUT) and a list of underused tract (UUT's)\n",
    "#  Loop over all tracts to find HD's that use this tract\n",
    "#     Among these HDs, determine which ones have this OUT on its boundary (OUT intersects MAPboundary or has nonHD neighbors)\n",
    "#       For each tract w this OUT as a boundary tract, ID which ones contain a neighbor of a non-HD UUT (an addable UUT)\n",
    "#          Score all qualifying HDs based on the centrality of its most proximate qualifying UUT\n",
    "#            For best-scoring HD, trade out OUT for UUT's until HD3pop recovered\n",
    "maxUUTuse = 0.8 #we'll prioritize subbing in UnderUsedMunis which are this much underused\n",
    "A0 = 1.000   #scorecard constant for encouraging underused munis vs. proximate munis\n",
    "A1 = 4.000   #penalty for using non-UnderUsedMuni when no real UUM available\n",
    "nLoopPrint = 10    \n",
    "maxOUTs = nWayOverusedTracts #20 #arbitrary number of OUTs to depress (debug)\n",
    "nOUTs = 0\n",
    "OUTusageStopLoop = 1.4 #when we get the OUT's usage below this, move on to another OUM\n",
    "while nOUTs <= maxOUTs and np.max(tractUse) > OUTusageStopLoop:\n",
    "\n",
    "    nOUTs +=1  #working on a new OUM\n",
    "    OUT = tractUse.index(np.max(tractUse))\n",
    "    print(\"the most overused tract\",OUT,\"with pop\",tractPop[OUT],\"and usage\",tractUse[OUT])\n",
    "    OUT_centerDistance = [0.]*nTracts  #we'll score HDs based on how far the OUT is from the HD centerpoint.  Fake as 0 if nonqualifying\n",
    "    sortedOUTCD = [0.]*nTracts\n",
    "    nQualifyingTracts = 0\n",
    "    for t in range(nTracts):\n",
    "        if OUT in HDtractList[t] and t != OUT:\n",
    "            isOUTboundaryTract = \"no\"\n",
    "            for tt in neighborList[OUT]:\n",
    "                if tt not in HDtractList[t]:\n",
    "                    isOUTboundaryTract = \"yes\"\n",
    "            if tractGeom[OUT].intersects(MAPboundary):\n",
    "                isOUTboundaryTract = \"yes\"\n",
    "            if isOUTboundaryTract == \"yes\":\n",
    "                OUT_centerDistance[t] = -1.*tractCP[t].distance(tractCP[OUT])  # -1 so low score is preferred (farthest away)\n",
    "                sortedOUTCD[t] = OUT_centerDistance[t]\n",
    "                nQualifyingTracts +=1\n",
    "    sortedOUTCD.sort()\n",
    "    T = 0\n",
    "    while T < nQualifyingTracts and tractUse[OUT] > OUTusageStopLoop:  \n",
    "        T += 1\n",
    "        t = OUT_centerDistance.index(sortedOUTCD[T])  #start with HD that is farthest from the OUT and work in\n",
    "        if OUT in HDtractList[t]:  #should be always true, but just in case\n",
    "            addScore = [0.]*nTracts  #underused tracts closer to HD tractCP will be assigned a more negative score\n",
    "            for tt in range(nTracts):\n",
    "                if tt not in HDtractList[t] and tractUse[tt] < 1.0:  #we'll check adjacency later.  For now, score all under-used tracts\n",
    "                    addScore[tt] = (tractUse[tt] - 1.) * abs(OUT_centerDistance[t]) / tractCP[t].distance(tractCP[tt]) \n",
    "        \n",
    "            OUTgroup = [OUT]  #include 1- and 2-level overUsed neighbors of the OUT to get rid of here\n",
    "            for tt in neighborList[OUT]:\n",
    "                if tt in HDtractList[t] and tractUse[tt] > 1.0 and tt not in OUTgroup and tt != t:  \n",
    "                    OUTgroup.append(tt)\n",
    "                for ttt in neighborList[tt]:  #now look at 2nd-level neighbors.  Use a higher tractUse bar to include in group\n",
    "                    if ttt in HDtractList[t] and tractUse[ttt] > 1.2 and ttt not in OUTgroup and ttt != t:\n",
    "                        OUTgroup.append(ttt)\n",
    "            popToShed = 0.\n",
    "            for tt in OUTgroup:\n",
    "                popToShed += tractPop[tt]*getFrac(tt,partialTractNo[t],partialTractUse[t])\n",
    "                HDtractList[t].remove(tt)\n",
    "                tractUse[tt] -= tractPop[t] / avgDistrictPop * getFrac(tt,partialTractNo[t],partialTractUse[t])\n",
    "            popGained = 0.\n",
    "            #print(\"We are shedding tracts\",OUTgroup,\"with total pop in HD\",popToShed,\"led by\",OUT,\"from Home District\",t)\n",
    "            while popToShed > 0.6*np.average(tractPop):  #get close enough; we can square up later\n",
    "                addableUUTlist = []\n",
    "                addableUUTscore = [0.]*nTracts\n",
    "                for tt in HDtractList[t]:  #OUT and friends already removed from this list, so their neighbors not checked\n",
    "                    for n in neighborList[tt]:\n",
    "                        if tt not in OUTgroup and n not in addableUUTlist and n not in HDtractList[t] and tractUse[n] < 1.0 :\n",
    "                            addableUUTlist.append(n)\n",
    "                            addableUUTscore[n] = addScore[n]\n",
    "                if addableUUTlist == []:  #no neighboring underUsed tracts.  relax underUse constraint\n",
    "                    print(\"no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\")\n",
    "                    for tt in HDtractList[t]:  #OUT and friends already removed from this list, so their neighbors not checked\n",
    "                        for n in neighborList[tt]:\n",
    "                            if tt not in OUTgroup and n not in addableUUTlist and n not in HDtractList[t] and tractUse[n] < 1.2 :\n",
    "                                addableUUTlist.append(n)\n",
    "                                addableUUTscore[n] = (tractUse[n] - 1.2) * abs(OUT_centerDistance[t]) / tractCP[t].distance(tractCP[n])                \n",
    "                tractToAdd = addableUUTscore.index(np.min(addableUUTscore))\n",
    "                if addableUUTlist == []:\n",
    "                    ohno = input(\"no nearby tracts found with tractUse < 1.2\")\n",
    "                if tractToAdd == 0 :  #should not happen; this is an overused tract\n",
    "                    ohno = input(\"Uh oh, code thinks we should be adding tract 0 which has a score of\",addScore[0])\n",
    "                HDtractList[t].append(tractToAdd)\n",
    "                popToShed -= tractPop[tractToAdd]\n",
    "                popGained += tractPop[tractToAdd]\n",
    "                tractUse[tractToAdd] += tractPop[t] / avgDistrictPop\n",
    "                #print(\"adding tract\",tractToAdd,\"with pop\",tractPop[tractToAdd],\"to HD\",t,\". PoptoShed is now\",popToShed)\n",
    "            if T%nLoopPrint == 0:\n",
    "                print(\"all done exchanging OUTs for UUTs for tract\",t,\". Total popGained =\",popGained,\n",
    "                      \"OUT and t0 usage now\",r3(tractUse[OUT]),r3(tractUse[0]))\n",
    "                            \n",
    "    print(\"we have now reduced\",OUT,\"usage to\",tractUse[OUT],\". Time for next OUT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fd844d35-8fed-4442-ab25-07525354def3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now recalculate all Home District leans\n",
      "I will now capture the patched HDpiece results in a csv file\n"
     ]
    }
   ],
   "source": [
    "print(\"we will now recalculate all Home District leans\")\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts\n",
    "HDarea = [0.]*nTracts\n",
    "HDweight = [0.]*nTracts\n",
    "HDtotVote = [0.]*nTracts\n",
    "\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "    totGOP = 0.   #zero out; the home tract is in the HDtractList\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  \n",
    "    for tt in HDtractList[t]:\n",
    "        fracUse = 1.\n",
    "        if partialTractNo[t] == tt:\n",
    "            fracUse = partialTractUse[t]\n",
    "        totGOP +=   fracUse*vtdTrump[tt]\n",
    "        totVote +=  fracUse*(vtdTrump[tt]+vtdBiden[tt])\n",
    "        totVAP +=   fracUse*tractVAP[tt]\n",
    "        totHisp +=  fracUse*tractHisp[tt]\n",
    "        totBlack += fracUse*tractBlack[tt]\n",
    "        HDarea[t] +=   fracUse*tractArea[tt]\n",
    "        HDvPop[t] +=   fracUse*tractPop[tt]\n",
    "    HDvGOP[t] = totGOP / totVote\n",
    "    HDvBlack[t] = totBlack / totVAP\n",
    "    HDvHisp[t] = totHisp / totVAP\n",
    "    HDtotVote[t] = totVote\n",
    "    \n",
    "\n",
    "print(\"I will now capture the patched HDpiece results in a csv file\")\n",
    "date = \"18Sepfrom14Sep\"\n",
    "tractNo = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"tractPop\":tractPop,\"HDwt\":HDweight,\n",
    "                    \"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDtotVote\":HDtotVote,\"HDvBlack\":HDvBlack,\"HDvHisp\":HDvHisp, \"HDarea\":HDarea,\n",
    "                    \"HDtractList\":HDtractList,\"partialNo\":partialTractNo,\"partialUse\":partialTractUse,\n",
    "                   \"vtdBiden\":vtdBiden,\"vtdTrump\":vtdTrump,\"neighborList\":neighborList} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HDpcL\"+date+\"patched.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "529db728-efe7-4877-a519-b17b05bbd875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now compute the tract usage\n",
      "and plot the tract usage distro\n",
      "1.0 0.12521 = avg,std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now compute the tract usage\")\n",
    "tractUse = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    for tt in HDtractList[t]:\n",
    "        tractUse[tt] += tractPop[t] / avgDistrictPop\n",
    "        if partialTractNo[t] == tt:\n",
    "            tractUse[tt] -= tractPop[t] / avgDistrictPop * (1. - partialTractUse[t])\n",
    "print(\"and plot the tract usage distro\")\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * HDweight[t]\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 * HDweight[t]\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "print(r3(avgTractUse),r5(sdTractUse),\"= avg,std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=HDweight)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c6d37b4c-9dbc-46e2-a553-f1a697644b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are 0.7 1.6\n",
      "We do not plot tracts with usage between 0.9 and 1.1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 3 used < 0.7 and 0 used more than 1.6\n"
     ]
    }
   ],
   "source": [
    "minUse = 0.7\n",
    "maxUse = 1.6\n",
    "minNoPlot = 0.9\n",
    "maxNoPlot = 1.1\n",
    "print(\"Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are\",minUse, maxUse)\n",
    "print(\"We do not plot tracts with usage between\",minNoPlot,\"and\",maxNoPlot)\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minNoPlot:\n",
    "        redd = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        gren = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        bluu = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy\n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor=(redd,gren,bluu) )\n",
    "    if tractUse[t] > maxNoPlot:\n",
    "        redd = 1\n",
    "        gren = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        bluu = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy            \n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "MAPboundary = Polygon([Point(-109,37),Point(-102.06,37),Point(-102.06,41),Point(-109,41)])\n",
    "MAPboundary = MAPboundary.buffer(0.01)\n",
    "plotPoly(MAPboundary)\n",
    "plt.show()\n",
    "nWayOverusedTracts = 0\n",
    "nWayUnderusedTracts = 0\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minUse:\n",
    "        nWayUnderusedTracts +=1\n",
    "    if tractUse[t] > maxUse:\n",
    "        nWayOverusedTracts +=1\n",
    "print(\"there are a total of\",nWayUnderusedTracts,\"used <\",minUse,\"and\",nWayOverusedTracts,\"used more than\",maxUse) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3b324fd6-01b6-438c-9342-3c5dadea3321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00236 0.43296 HD avg vs true 0.43061\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden))\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ec3f408-c94d-4c43-913e-aa1220c5ec1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the correlation of HD area to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here is the correlation of tract usage to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is the correlation of HD area to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )   #need to calculate HD area before do\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()\n",
    "print(\"... and here is the correlation of tract usage to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "33362e3b-1cf7-4ddb-a08c-0c5d5f6ba7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA5+UlEQVR4nO29fZRU5Zno+3uqGojMIHQQlW/EDxLBmIEWMJNETWImeDGOGoeoZ0wyo8QzOjcfN+eYiXe4XmZNjmcyWdfJDCdEOa5ZOUvRKBqNSyfqhPiR2Eg3J0pjArYg0IIK2CATlO6ueu4f+4Ndu/betXd3VXUV/fzWQrpq73fX+1bj+7zPt6gqhmEYhpGW3HBPwDAMw2guTHAYhmEYmTDBYRiGYWTCBIdhGIaRCRMchmEYRiZahnsC9eCkk07SWbNmDfc0DMMwmorOzs79qjop/P6IEByzZs2io6NjuKdhGIbRVIjIzqj3zVRlGIZhZMIEh2EYhpEJExyGYRhGJkxwGIZhGJkwwWEYhmFkwgSHYRiGkQkTHIbRIHTu7GXV+m46d/YO91QMI5ERkcdhGI1O585erl3TTt9AkdEtOe65fjELZrYO97QMIxLTOAyjAWjffoC+gSJFhf6BIu3bDwz3lAwjFhMchtEALJ49kdEtOfICo1pyLJ49cbinZBixmKnKMBqABTNbuef6xbRvP8Di2RPNTGU0NCY4DKNBWDCz1QSG0RSYqcowDMPIhAkOwzAMIxMmOAzDMIxMmOAwDMMwMmGCwzAMw8iECQ7DMAwjEyY4DMMwjEyY4DAMwzAyYYLDMAzDyIQJDsMwDCMTJjgMwzCMTJjgMAzDMDJRU8EhIp8Tka0i0i0i3464LiLyA/f6yyIyv9JYEfmoiLSLyG9EpENEFtZyDYZhGEYpNRMcIpIHVgFLgLOBq0Xk7NBtS4Az3T/LgR+mGPsPwP+rqh8FVrivDcMwjDpRS41jIdCtqttVtQ+4D7gsdM9lwI/VoR2YICKTK4xV4ET35/HAnhquwTAMwwhRy34cU4Hdgdc9wKIU90ytMPbrwM9F5B9xBN/Hoj5cRJbjaDHMmDFjUAswDMMwyqmlxiER72nKe5LG/mfgG6o6HfgG8D+jPlxV71TVNlVtmzRpUsopG4ZhGJWopeDoAaYHXk+j3KwUd0/S2C8BD7k/P4Bj1jIMwzDqRC0Fx0bgTBE5TURGA18EHg3d8yhwnRtdtRg4pKp7K4zdA1zg/vwp4NUarsEwDMMIUTMfh6oOiMjNwM+BPHC3qm4RkRvd66uBx4FLgG7gCPCVpLHuo28A/klEWoD3cf0YhmEYRn0Q1bDb4fijra1NOzo6hnsahmEYTYWIdKpqW/h9yxw3DMMwMmGCwzAMw8iECQ7DMAwjEyY4DMMwjEyY4DAMwzAyYYLDMAzDyIQJDsMwDCMTJjgMwzCMTJjgMAzDMDJhgsMwmojOnb2sWt9N587e4Z6KMYKpZT8OwzCqSOfOXq5d007fQJHRLTnuuX4xC2a2Dve0jBGIaRyG0SS0bz9A30CRokL/QJH27QeGe0rGCMUEh2E0CYtnT2R0S468wKiWHItnTxzuKRkjFDNVGUYT0Lmzl/btB1ixdC69R/pYPHuimamMYcMEh2E0OObbMBoNM1UZRoNjvg2j0TDBYRgNjvk2jEbDTFWG0eAsmNnKPdcvpn37AfNtGA2BCQ7DaAIWzGw1gWE0DGaqMgzDMDJhgsMwDMPIhAkOwzAMIxMmOAyjybBCh8ZwY85xw2giLBnQaARM4zCMJsKSAY1GwASHYTQRlgxoNAJmqjKMJsKSAY1GwASHYTQZlgxoDDdmqjIMwzAyYYLDMAzDyIQJDsMwDCMTJjgMwzCMTJjgMBoWy5A2jMbEoqqMhsQypLPh9SS3EF2jHpjgMBqSqAxp2xCjMSFr1BszVRkNiWVIp8fKkBj1pqaCQ0Q+JyJbRaRbRL4dcV1E5Afu9ZdFZH6asSLy1+61LSLyD7VcgzE8eBnS3/zsHDtBV8CErFFvamaqEpE8sAq4GOgBNorIo6r6SuC2JcCZ7p9FwA+BRUljReQi4DLgI6p6VEROrtUajOHFMqTTYWVIjHpTSx/HQqBbVbcDiMh9OBt+UHBcBvxYVRVoF5EJIjIZmJUw9j8Dt6vqUQBVfbuGazCMpsCErFFPammqmgrsDrzucd9Lc0/S2LOAT4jIBhF5RkTOi/pwEVkuIh0i0rFv374hLMMwDMMIUkvBIRHvacp7ksa2AK3AYuC/AD8RkbL7VfVOVW1T1bZJkyaln7VhNCCW02I0ErU0VfUA0wOvpwF7Ut4zOmFsD/CQa956UUSKwEmAqRXGcYmF2xqNRi01jo3AmSJymoiMBr4IPBq651HgOje6ajFwSFX3Vhj7U+BTACJyFo6Q2V/DdRgNwkg9dVu4rdFo1EzjUNUBEbkZ+DmQB+5W1S0icqN7fTXwOHAJ0A0cAb6SNNZ99N3A3SLSBfQBX3K1D+M4ZiSfur1w2/6BooXbGg1BTTPHVfVxHOEQfG914GcFbko71n2/D/hP1Z2p0egET91H+4us29QzYgSHhdsajYaVHDGagsWzJ9KSE/oKigIPdvZw5fxpI2YTDYbbWl0qY7gxwWE0BQtmtnJV23Tu3bALBQqFkVe/qnNnL+s29fBgZw8DhZFnsjMaB6tVZTQNV8yfxphRI7O0hufjWbthlznKjWHHNA6jaThebf1pTE+ej8eLAhFGnvA0GoeKgkNETlPVHZXeM4x6cLyV1kgbLRaMrMrnhKvapnPFCPLxGI1FGo1jHTA/9N6DwILqT8cwKnM8OYfTRosdr9qW0ZzECg4R+RAwFxgvIlcELp0IfKDWEzOMKI63fI7WsaMpuvYnBR7o2B0bLXa8aVtG85LkHJ8DLAUmAJcG/swHbqj5zAwjgkbMoh5KRnvvkb6S1wMFbYg1GUYSsRqHqj4CPCIi56vqC3Wck2HE0mhZ1EPVgBbPnkhLXhgoOGpHI6zJMCqRxsfRLSLfwemR4d+vqn9Rq0kZRhyNZuuvRm90T+3PC9x26dxhX5NhVCKN4HgEeA54GijUdjqGUZlGsvUPVQNq336AgeKxUmth05VhNCJpBMdYVb2l5jMxjCZkqBpQo5neDCMNaQTHYyJyiVt00DCMEEPRgBrN9GYYaUgKxz3MsW583xGRo0C/+1pV9cT6TNEwjm88YeFFU5nwMBqdpKiqcfWciGGMVLzIrKP9Tlb4ysvmcc2iGcM9LcOIJU3JkXDWOMAhYKeqDlR/SoYxsmjffoCj/U4dqoGisuKRLuacOs40D6NhSePj+B84SX+b3dfnAC8BE0XkRlV9slaTM4xGoxblThbPnkg+J350VVF1xJWMN5qLNGXVXwf+SFUXqOoC4KNAF/AZ4B9qNzXDaCw8k9L3n9zKtWvaq9b7fMHMVlZeNo+WnJATGJ0QXTVS+64bjUUajeNDgX7fqOorIvJHqrpdRGo4NcMoZbiLG1Yj2S+OOaeO48/Om45AWdVbb92tY0ez8rEtx02dLqN5SSM4torID4H73NfLgG0iMgYnysowak4jFDesVc5FeG1XzJ8WeS0nQlG1JoLLMLKQRnB8Gfgr4Os4objPA9/CERoX1WpihhEkbfnxWuGd+lcsnUvvkT5ax46uWvhskiYTvFZUJZ8T8qglCxrDSkXBoarvAd93/4T5j6rPyDAiWDx7Ii05oa+gKPBgZ09s+fFqE9YIViydW2YyAjKZ0YJmN0+T6esvIiK0jh3t3xdcN4AILDtvhjVxMoaVWOe4iPzE/XuziLwc/lO/KRqGc6q/qs3xAQAUCvUrqR7WCJ7o2lvyet2mnkxO87CTHWDF0rnkco4pauVjW/xnhNetRWXKhBNYMLPVHOXGsJGkcXzN/XtpPSZiGJW4Yv401m3qqXtdp7BvY8m8yWx8/R3/tUAmp3lcT5E4/0XUuu/dsIu//elmigqj8sLa5eebBmLUjaTM8b3u3ztFZCZwpqo+LSInJI0zjFoQ9DF07TlEPeP5oupJzTl1nP8ayCTQ4pzscY537/PXbepBgK1vHuZvf7oZ13pFX0Hr7vMxRjaiqsk3iNwALAc+qKqni8iZwGpV/XQ9JlgN2tratKOjY7inYQySoI+hJScgwkChsUJSs4YKR92f9IzgdyDgCw2PaxfN4O8vP6dayzEMAESkU1Xbwu+n0RxuAhYCGwBU9VURObnK8zOMWEpMOwUFHAd5I4WkZq2QG3V/0jOC30GYUXkpCeE1jFqTJnP8qKr63WVEpAWnaq5h1AXPtJMXZ5Mc5f08gkJSve8gbKI7d9p47jP/hlFn0mgcz7itY08QkYtxcjp+VttpGYZDlG9j7pTx9B7pG1H9K4J+jgc7eygUHF/ICms1awwDaXwcOeAvgc/iJAD+HFijlQY2EObjaE6awbcxHAx36RVj5DAUH8eFwD2qelfVZ2UYCTSDb2M4sMZPxnCTtuTIahE5ADzn/nleVS3ryKgpwbDVvKtxeCaa48G3MVjNoRHqdhkjmzQlR64DEJEpwBeAVcCUNGMNYyiE8ycgW1mPWjJUc1GWrn/hz6pllV7DSEOaDoD/CfgETgOn/cC/4GgdhlFzwiGqjbBBVuPEX6nrnycsDr/Xz5rnd1BU9T+rVlV6DSMtabSGO4DXgNXAelV9vZYTMkYeWU7vlZLk6qGRPLSpx9/0B3viT+r6F9RGghEoR/udz7rpojPKMtkNo56kMVWdJCJzgU8Cf+9mjm9V1T+vNFZEPgf8E5DHicS6PXRd3OuXAEeAL6vqppRjvwV8D5ikqvsrrtRoSLKc3pPurZfdv3NnLw907PY39Hx+cCf+BTNbuf7jp3Hnc9tRLe36FxRMQRT8yrlZEw4No5pUTAAUkROBGcBMYBYwHiimGJfH8YcsAc4GrhaRs0O3LQHOdP8sB36YZqyITAcuBnZVmofR2MQV/Mt6b5bnDHW+npYgwBcWDK68+b0bdrHm+R2oQj4nrFg619c2goIpiAC9R/oirhhGfUmTOf48cCnwMrBMVeeo6pdSjFsIdKvqdjfz/D7gstA9lwE/Vod2YIKITE4x9v8D/iuWwT6sVKOsd0lWeAV7fdK9WZ4zFIKfM2ZUjitD3frSfB+dO3tZ8UgXA0UnvLio6guEsGA6b1ar34t8zKj060ozFyvLbgyWNKaqjwzy2VOB3YHXPcCiFPdMTRorIp8H3lDVl6zn+fBRLdNQVOXZwdyb5TlDIe5z4r6PKL9L+/YDFAJFp3IivkAIhyCfdco4Lv+jaZky5dP8biyk1xgKtQypjdrVwxpC3D2R74vIWOBWnCz25A8XWY5j/mLGjOgwR2PwVDMktJK9Prz5JjnFb7rojIrjh0rUHOJMZVGb8+LZExkzyun4l3NDcb3nhUuLrH1xV+aNPc3vxkJ6jaFQS8HRA0wPvJ4G7El5z+iY908HTgM8bWMasElEFqrqm8EHq+qdwJ3glBwZ6mKMUuoVElrpZDzU69Ui/H20jh3NHU9vi9ycK2lHC2a28pDb3yMucitJGKb53VhIrzEUaik4NgJnishpwBvAF4FrQvc8CtwsIvfhmKIOqepeEdkXNVZVtwB+SXcReR1os6iq+lMv01DcydjbOPccfC/x5FzLk3V48/a+j9axo1n52BY/MioX4XdJ0rIqRW5Vii7zikImmbfq9fszjk/SJABOAm7Aiajy71fVv0gap6oDInIzTlHEPHC3qm4RkRvd66uBx3FCcbtxwnG/kjQ28+qMmlKPkNCok3G4+GFLPhdbiqRWJ+u4zXvBzFZWre+mz9UWcsAfn3ESX//MWZlMTUmRW0nCNIt2ZSG9xmBJo3E8gpMp/jRQyPJwVX0cRzgE31sd+FlxGkWlGhtxz6ws8zGaj6iTsbcxFxUKRWXZwulMnXBCrNmnFifrJE0mLKy+/pmzAFi1vjtxDp620Dp2dMn4K0NNmqLMYqvWd1fUvgyjWqQRHGNV9Zaaz8QwYgifjMMb55Xzk3Mp0pysszrQkzSZBTNbWbF0Lk907WXJvMnAMSd5Tpx+IsvOm1FSmyqsLSSZmqLMYmm0L8OoFmkEx2MicomrARjGsFNtLWIwDvSkOXTu7PU3842vv8OV86f5mkBR4aWeQ7zUsxmAOaeOi/TV9B7pi4wQC35+Vu3LMKpFGsHxNeA7InIU6Mcxu6qqnljTmRmGS5Q2UE37/GAd6HFzCD9PcUqKvN9fWnDh/o272PrW4TJtIZ8T9hx8j86dvRXnkVX7MoxqkCYBcFw9JmIYUdQjnLbaDvSozfzK+dNY/cxrPPXKW/59p5z4ATa/cahEWwD8/I11m3pSrfeK+dMQ928TGkY9iBUcIvIhVf2diMyPuu4VIzSMWlKPRLWhmr6CTm3PLxH1vLuua+PeDbt838ecU8fx7Kv7SgRM+/YDDBTSrTfc02PulPEmOIy6kKRxfBMn8/r7EdcU+FRNZmQYAeqVqDZY09e9G3ax4pEuCm7dqZzga0ZRPoprFpU6xYNOdO/z0663Uk8Pw6gVsYJDVZe7f19Uv+kYRimNnKgWLFbokUUzCjvRvU0/yekefD+pp4dh1JI0CYAfAP4K+DiOpvEcsFpV36/x3AwDGJw2UI+mTg9t6ikRGnCsyNpLuw9WdG7HmeGi1utpNsFOgAtmtrLysnkl71sIrlEP0kRV/Rg4DPyz+/pq4H8BV9VqUoYRJmuXwKE61Ct9nlcWxCOfEy79yGR+9tIeCgpPvvIWv9y2j7U3xH92WjNcWLPpCwiZaxbN8EN60yQXNprWZjQnaQTHHFU9N/B6vYi8VKsJGQaUbnQQXWU26t4FM1sH7VAPOrm9WlN5t3pt0C8B5WVBlp3n5E8EFZBKn13JDBesxxVXht17TqXqwlZC3agmaQTH/xaRxW6jJURkEfCr2k7LGMmEN7pgAl14M47aFJNO8nEn7+BzciK+szvO6RwVcgswKi/0FZxNPo0zP27TD9fjGtWSY2CgvAx7GqyEulFtksJxN+P4NEYB14nILvf1TOCV+kzPGInEJdBFCYKoTfGmi87I1Gwp/JyiKiL43WMKReWOp7eVFCqM0xbWLj+fdZt6hpxXEZzPUDPCrYS6UW2SNI6ldZuF0fRU04bubXR9/UVEhHlTxvs5DuHnxxX8i2rqVKkwYUvumLaQE0CEoqt5/Kp7Pxtff6dE2ERpC9XKaI/6DsLmsijisuwbNTLNaE7EKVB7fNPW1qYdHR3DPY3jllrY0OOiiOI+P1zwL6mpkydkwtdvfXgz927YhQJ5gWULZ7D7nSM8/+p+v0T6//UncxJrSFWT2x//LXc+tx1Vp994pe/VfBlGtRGRTlVtC7+fG47JGMcXcW1Th0LvkT6KqhQV+vqLrPzZFm59eDOdO3vL7l0ws5WbLjqD3iN9ifPwTt7f/OycyE31ivnTGDMqR95tvHTl/GksmTfZb6hUBFrHjgacTXrV+m46d/Zy74Zd/Pn/3MC9G3ZlWmPwGVHX1jy/g6I6FrO+FN9rLX4PhhFFLTsAGk1KNUuMD5agqaaIV1H2EA909nDbpXPp2nOozI+QZh5JpqQok0779gPkxEnsy4kj0Eod6TDg1i587lWnEWVak1KSdtC+/UBiJFXcd9aSE/oLSj5X+X7DGCwmOIwSql1ifLBz8NqfPtG11zcVgXPy/tufbsZ1RfBAZ4+fKxHuU+GduJNCdysRFGA5Ef+5wTLpQZ7o2psqr6JSpNPi2RMZM8r93CyRVJ5XX6TirYYxWExwGCVUu8R4VqIaGm3YfsB3WudzjsPaIzxH7+8o4RcOcb2qbbqvsXTu7GXdph4e7OxhoFA6bsXSub6/ZeVjW1ixdK6v2UhA4wCY+AejWfajFygUNTYHBOK1o6Bgu+f6xX6E1pxTKxep9gokKlAoWNitUTtMcBglDHfoZlhw9R7pY+3y81n9zGu8/e77nD97Inf/akdirkSc8Au+31dQ7t3glC5fsXSun/DniaTguKC/xZtTUMPa+uZhnujay9zJJ7Lm+R1+YqCXAwKUdfOL0tLCgu3COSfzy237GCgUU5VYH+7fnTFyMMFhlDDcoZvBzc9raLT1zcM89+o++gaKbH3rMLd9fl6kjyPqGcEN1HvfExCKIwie6NpL38AxoSEQOS74vKCG5ZX+WLW+u8QvAU4ORjA6LNwSNuzXCAq2JwO9O9Jof8P9uzNGDhaOawwbSVncQbNRTsQ/8ecFvvnZyiGx4WcHQ3a79hziwc4evzf3iqVzue3RrhJz2N+FTExpfCPB/hiKI4DyuWNzzwE593VSuHBQ88F9TppwXMOoNnHhuKZxGMNCkhPeMyt5DY1QJZcTBE1tgvGetfqZ19ix7z/Y+c4RCsVjG/aV86exblMP+w8fZcueQ1w452T/hF8sKl17DpU9L0uQgCeg9h8+yi+37aNQcBL5giavsAbh+VOCzn9whM2KpXOtgKHRMJjgMIaFNFFFQfNQ2MQDyRtm585elv3o1yWOazj2WYtnT+TBjt0BLePYPQo80LHbrz+VZVP2BIzXa+NovxOy++kPn8KFc05m5WNbEn0QvUf6KLMBqNJ7pC/y8+IEsAkTo5aY4DCGhUqO3DSVY+M0ls6dvdzx9LYyoQEggZDa/sCxvhC6d6CgrH7mNdb/7u1U2ethgt35Cgq/+N3bfPWC0yv6IML5KzlJLpYYl/RnGeRGLTHBYQwLaRy5wQKEwdfee96GebTfiToKRiYd7S+XGk4i37GQ2mAl25a8k/cw4GkgeeEXv3vbd3b3pXBOB4nrznfTRWekdnAHe5hn6ekxmJBq01CMLJjgMIaNofSRCBYlVOAnHbvZf/gob737vh8hlQMmT/gAew69jwaS9YJhvsFKtoD/GigpIVIpc9tz6AcjvQbbnS9LTkycAM4Slms1roysmOAwGpZKJ+cPTz6Rl3ocJ/ZAKHxVgJaWHBfOOZm1L+6KDLWNq27bubOX1c+8VuJruP7jpyU6p6++8wVfe/Gy2YPd+cKZ7NU84YfXkTUs1/p1GFkxwWE0DOHNNCm7OmiOCrTO8FEAVcaNaSkpC3Lx2afw1QtOTxVS65ED3j064JdrD4fQ3vH0thJ/Sbh/OFCWDZ9UxTfLdxRHGm3Oe44lDhpZMcFhNARx5pKok7N3QvbMUedMG8+Wve/6/gmPQlHZsvddX7DkgHOnT0jcUIPPBldzyYufUxIsVQJE5l1UajblJRwOprVtUkDAUHqyW+KgkQUTHEYs9XSYxplLok7OZaG6l84F8PMyvLyJUS05lsybzMbX30l9mg5nrl/VNh2AtS/uKitV4rW0DQqweVPHl2Wzh+ebdU6VvqOsPorgc953S9avuHRu3fqMGM2PCQ4jkmo7TCsJoSzmkjhNJO70naZabfjZUU7zcKmScEvbFZdGJ+lFzTfLnKK+I68ci7fWLBpMuNvhSz2HuPqudr/KsGFUwkqOGJGsWt/N95/cmqnMB0QLiLRCqNYaTtrnR80X8MugeNqM9349TTzhcizBYojBeVWay60Pb+aeQNSYAN+qY3dDozmwkiNGJgbjMI0TEHE5F2GyhKFmJYsGFT7Br9vUw9QJJ3Dl/GmRvc/reUoPl2PpKyhPvfIWo/LCFxfOiCz66BEUnHOnjC+51pK3xk9GekxwGJGkydyOc1qHTSbhnIsHOx3/wGAcu4PloYCpqZI5J2wSCvfoGO5TeVSV30JRmTLhhFTRYvmc8KkPnex3NhTgqrbpZqYyUmM9xw2fcA/sBTNbIzOdvU3o+09u5do17f793obm9ez2TrALZrZyVdt0P7HOazKU9Kxqr+uBjt1+5FM+n86H8s3PzuGqtun+6b4efbyT+pCH53f1ohkl33fr2NGxY4MlUAaKyr//9i3yOXEq+Oadv2vx3RvHJ6ZxGMDQTDnBCKg4LeUKtxpt2PSVRRMYLO3bD/ilPwT4woJ4c45HsFhh1LxrQZbfgTc/z3TWOnZ0WW6I12BqybzJESVQnP8oTvLk2hd3pWoWZRhQY8EhIp8D/gnIA2tU9fbQdXGvXwIcAb6sqpuSxorI94BLgT7gNeArqnqwlusYCcQVy4sSAkn+jzg/RZRQCWsCuAUI05LWxFXSNzwnzAvZ95MI145q336ArW8eTqwhNVjTW1Ar6OtPJ0S973vV+u6S39/qZ17jKTeT/rlX9/Pdy88pKYHi9TjxsKxxIws1ExwikgdWARcDPcBGEXlUVV8J3LYEONP9swj4IbCowtingL9R1QER+e/A3wC31GodI4WwMGgdOzqxX0aWhLHgRhr0DwQ1AXDs9Csf28KcU8cNKokt6XQe7hue5jOC46E02S8nJDZjCmaJVypU6NE6drQvRIvu67Qsnj2Rlrzrl8nnePvd90uuP9G1l//1l4tKSqCsfGyLX4VXcJpOmYPcSEMtNY6FQLeqbgcQkfuAy4Cg4LgM+LE6McHtIjJBRCYDs+LGquqTgfHtwBdquIYRQ1gYVMoNSBsBValQYdDJC+lPvWlyF4KFB4HYJkpxIcTB94LagPOs6M8taf/aXywpcljJDNR7pM93WOeE2B4csbgaRLFYZExLqftyybzJQOnvbc6p41i3qYcHOnY7WfciGEYaaik4pgK7A697cLSKSvdMTTkW4C+A+6M+XESWA8sBZsyYEXWLESC4UQK8cfA9WvI5Pzcg6iSaxiSTtMEHk+2C+RFpTr2VwoXDhQdb8uKvRwQe3tTDS7sPMvukP2DN8ztKNnco72cR1AagvC951LzCHf/WbeqpWhJkmIc29dDvRq0NFOHF13vJC8ybOp5l580oaYPrsWBmKw9t6mHAHecFLZipyqhELQVH1PElnG0Yd0/FsSJyKzAA3BP14ap6J3AnOAmAlSY7kglqBS05ARE/uSwuNyCtqShNwybPyRvUDioJpXCGd5iyRk0F5c8WTWff4aM89cpbdO/7Pd37fl8ypi/g24ny93jaAMDMiWNZ/snyYolhn4jX8S+fE/9kP6olF5mlndUE6FHmK/LWrDB36njmnDoutkBjONrMi8yymlVGErUUHD3A9MDracCelPeMThorIl8ClgKf1pGQ+l5jSrSCgqLuVhLMDYgy3aQtc3HF/Gkl5TviNqaHNvXQN1DkgY7dvvCqZOLxxoQjghbPnljSqMlzij/RtTf2ewj23AiX9pg7ZXxJZ75d7xyJ9ZWEzUHt2w/w0u6Dftn3voEiD1UxCTLsKwrS/dZhlv3oBV+jCvpcwtFmF5w1adBVe42RRS0Fx0bgTBE5DXgD+CJwTeieR4GbXR/GIuCQqu4VkX1xY91oq1uAC1T1SA3nP2IItyv18PId7t2wq8xWn8asEtZK5k4ZH7sxhYUXrvhKEkpJRf/atx/gts/PY/3Wt/nF795GXaf4l8+fxXOv7i97Vj4nrLxsnv85QRPa2hd3+ZvuE117+VX3ft+HccfT2/j6Z86qGF31xsH3Sq51vXGIzp29VdmYwwmLivMd5gQ6dvb6WtL7/UVufXgzAKPywm2fn1fyOzx53Bjry2GkomaCw416uhn4OU5I7d2qukVEbnSvrwYexwnF7cYJx/1K0lj30f8CjAGecqJ5aVfVG2u1jpGAZyK54+lt/qbo5TsArHikyz+Zeuacmy46o6JZJUs58aDwEnE0hGJRE239UcIrLOSunD8NDfgZxp0wiu9efg73b9zFmJYcm3YfpFBQ8uJoB3BswxcoSf7rPdLH1z9zFhtff8cXsr/q3s/G199JFV3VksPvg/5yzyGuvvMF1i4/f0ibszfXoCax9c3DrHiki0JRy8xX3uu+grJ+69slZrWuPYcq+rUMA2qcx6Gqj+MIh+B7qwM/K3BT2rHu+1aFrQYsmNnqb4reRuwllxUCZpCgOSeoLXgEq8qGN/a5k0/khdcOoKpIKGcjHDIrwBcXzmDulPGRPce910HhBeVCLlzB1hNy1yyawar13XTs7PVP6Os29QCU+HvCG2mUkK0UXdU/UKRrzyFyuRwUHcmhOJt3XN2uNCTVBitqqdAQ8YOufH7xu7e58YLTWTx7YsmaK9W8MgzLHDd8opyzW988TD4nFIpaYs6JqtJaBL+Z0v0du1nWNt0/CXuOYm9Tj8rZ6D3S50cheaflSjb3oE9g1fruMiEXLkzo3ed1vgvX0BKOOcYLRWXZwulMnXBCWVHDsJBNiq4a1ZLztZcwQwmATaoNFjY95gDJS0mzK1UtCwaoVPPKMMAEhxEiuBF37uxl5WNbSoTGNYtmlBTM8/MvCqUn3AG34dGYUTlfGPUNHNs4o/wXUZtt1j4TY0YdM3d96kMnl6wp6oR+Vdt07t2wy5/P24eP0pIT+gvOmq+MOXlXioAKR1et3/o2IoK4moAXzusFDAyGOD+T99krf7bF78leULh4jvN9eD6f0YEx1jrWyIIJDsMnLnJKcU6nXXsOsWp9N3sOvlfWXnVUvlTjgFLhED4F56Q8DyLK9JSmTlQw0W/F0rl07TnEg509PP3bt3j21X2R5d29eV0xfxoPdOz2tY71W98+ljCIE7XlzS1MpQgo71ownySfE274+GmMO2HUkENek4TXgpmtzJs63hccACePG8PfX35OZKiz5/hfMm+yaRtGRUxwGEC0vTyuvHjQ9u+1V507ZTxdew7R/dbhkkgeLzIrmHex//BRJo0bExmeG96MK5V2X7eph594mc84J+cLz5rkd+iLMuGE/R1BraPgPscr/nfPhl3cv3G3r21lJZxPUiwq404YVbXS7EnC64r503ig85jg9b7v8BhPs+wbKLLx9Xf8IAHrQW7EYYLDAEqr1HrNlr57+Tn+xr3n4Ht+3+3+gvKZs0/mo9Mn+FqAJ3RygbIVXmRWcOPxPicn8Opbh/lNz6GyfI3wiTgu1DVsLgPHtPXUK29FllCPO6EHK/fm3QTI/oBGNVBUVjzSlam+lUc4n6SepqAFM1tZe0N5YclgAENYEzva7xRIfO7VfZbPYcRigsMoyyD2HMWefd/bcIImnWe27ePGC5zM6WBlVlTJ5QRB/cgsj2C9p4I6ZTE8vJyIJfMmp0pCC5rRwgRNaGHBFSWI4kxk92/c7Tvbi64jOesGumBmK2uXn1+2WdeLsM8qaDZ7oLOHtTcsLgsS8Hwgls9hxGGCw4jMPA7XLSo36cSbgOIqwi6ePZGcG6EVxsuJeOG1A35kVVKb2XDex4KZrRwdKPJyzyFfcOQEv0FRpQS9sEBZMLOVeVPGl+SEDFZTGEw2eBbiyrNE+ayCZjMv4/67l59T8rvVYqnwN2e5EcYEh5HKcQ3xzZjS1lja+uZhiiGhkc/BvCnj2fzGIV9j8axdCty/cTfzpowv8y+E8z5efuMQK5bOZetbW0qSCOMaFKWptXXNohl+yZBKm7L32kukS9IuqtkqN24dcT6roNkMjmmW4d9tlnLwxsjDBIdRFjoat2GE7wsm5kU5XMMb64pHuspMS8vOm8GV86dx7Zp2f9P65JmT/LpOhQT/QjDvw8vsjvTJpEjQi2tcFbWuYP6KlxW+8rEtZf4WzxSUVWBlIS6XI+r9my46g7XLzy8N0y2krwRgGB4mOAwg2ZwSdUJO2vyiNsdwBjrglwSJ8jH84ndvB9qcRvsX4qKkgk7gpAS9YOOjpMZV4XWF+4d4pVTCQjFKYFW7VW5cLkdSjseKS+eWCOrgNRMYRhpMcBiJdO7s5eq7nA01nxP+7rJ59B7pS0zMC0fprNvkmEOCyXmf/vApXDjn5DKtxSPY5jTOv1Apj6HiCdqrwaHKlj2HKiYbhh3yXhLfknmTS+pXeYQFVlQZ86H6D+LWOeTvxjASMMFhJOKVLQfHbHTrTzfz1U/MTsw0jirlceX8aWVahXd6D2aleyT5F4IknZKTQnnveHobA25ZE6+8SaXs6XBey1Vt030/RrAlq+fjCNfZCpcxD0d8DZa4dQ7muzGMNJjgMBIpq66qcNdz2/n0h0/xk/iifCFREVg3XXSGf++q9d2+ySYuT6IWm5tXPdcTFl4gQLimVdxGnHSKr2SuC5uPrhxCuZGspI28qqbj3jh+McFhJHLl/Gn8ZOMuvxw4ODkYT73yFmNGxddaiovA8lg8eyL5nJQUPUzqbVENPAd98NT/x2ecVPKZlT47rTCLc04Ph4kobeSV5+S3xD+jErnKtxgjkc6dvaxa3w3A/V/9GBeffQr5nPh1nILO3Si80/k3Pzsntqrtysvm0eI+U3HyOK5d007nzt7IZ6adc9z4sINehNS1mSo9O4ynXeSgpIT8gpmtJZpXPYjyOYXfj+uXYhhRmMZhlBF1Qr3ruraSUNRgj4o480al07nnxwj2tqjUVS+OqC6FYZPMGwffY5RrKnJTRmLbv1b6PtJoJl6eiRdS7K05/OxaayBxPqew6cxz8luVXKMSJjiMMpJyHKL6W4TNHVkSxxbMbE3dVS+OsAmqLxQVFdz4W3LCR6YdSzhMExKbpb96kN4jfb4vJcqPU+2cjjiSfE5h01magATDMFOVUUbYzHL4vX6uXdPO95/cyrVr2gF8c0twU+3rL7LikS7/vjRmnWDr0z8+8yRyQplJpRJJXQq968FGRXOnjmd0S458TIZ83PeR9v7guHzuWNHHYqBxUnhetTYNXeGGQ4fXEDSdmWPcSItpHEYZ4XIea57fUZKhHdd8SUTK7oP48tzhE/eXz5/Fr7v3A+WFFpMINnDKBboUBq+Ho5k8rSmcAR/3fQzGqe35ceLyUeKS9AZL0sYflWQZLGdfL+3HOD4wwWFEEiznEa54G9d86fB7/ax5fge49x1+r59lP3oh1u8Q1lbWPL+DQBmlskKLcVTa2JOup90sBxsanJSPMtREvKCgSLOWYFZ9VGa/Fx7d128VcY1kTHAYPsGNqHXsaHIiqCs0rk/oWhcsQ1IoKjmBj0wdz13P7/BNSGG/A5RrKyURT2QzC1Xa2KOuD9Z3kZVaJOKFN/8r50+LXUtcZ8fgva1jR/s5O0Xwo8AMIwoTHAZQ7kD2HLrg+AX+9YXXE0/kwXIc4V4bHuHNKKytrH52u3/t4rNP4atuv49aUW1TUT0Jb/5xme9eD47+gjIqL6xdfn7kutu3H/D9SzlxNE7DiMMExwjHC7F9ccc7vN/vZPkFy26D428IagxhE4l3Yh3dkiurEOs/ww19BUqirjzBcMfT2/x8jpzAudMn1NxU0sw1m5L8NsG1rNvU4/8++wpa1tkxeG9LTugvKC05J+8k6AMxjCAmOEYw4Y5wSXiRSmHNBJGS8uJdew6VdM7z8Gzn4VwLoKTibE4YUtOkrDRrzaY4oecJdm/Tl9A4IbrMyLpNPSWmqtt+tqWspa9heJjgGMGEO8LFkQ9EKgXbxDpj1c8i7z3Sx3cvP6ekc54nXAqF+Kgrz8SVo7wESBzDETraaOGqUUIvKlItnxOKRSdgYe6U8ZFlRoKaYqGgFAK/10rRccbIwwTHCCboEI0iJ9A2s5UzTxnHnFPHAeUVYj2hkM8Jew6+R+fO3rJIIjhmzlr52JYyO3w4e7lSeGxUjaWhdKtLIxCaJVw1KlKtWFTyOeG2S+eWlcQP9xIRYFTe1STdoAUvj6fR127UDxMcI5jeI32+XwEo+RmcF5t2HaTDNWV4G0Y4H8ArQxJu0xqM6gGYc+q4SPNKsKtgmiJ7UUmHcSG/lUgrEOoVgTVUvPIi/QWnBW9R1S2vor5wjSszEiwVv/XNw6nyeIyRiQmOEUy4B3U+L3xqzsn8ctu+WNNSUCB4AkA4FtkT3liiakjddNEZJfPwnlliBkvYoColHWbZ1NIKhKaKwBLnCCA5Ie9qhMEOiWnKjLRvP5Aqj8cYmZjgGMGEaxhpUTl3+gS+esHpZaYlzxR174ZddO055PfcbskJRY5pKsGudpVqSIVJuzkHN78481daBvOZjWznb99+gIFC0f99fmHhdKZOOCGxj3qUryT8vQzVHGgcX5jgGOFcMX8aD3Tspr/g2MHDYbJzTh3nm6I8ARM0aQWd64LT1Q6cchZ7Dr6XWEMqTJbNOTzHwW7og/3MRiUqTHewGekmLIw4THAYvmnD+bsUL9PYO8UCkY5Uzxwyb8p4rr6r3dFS8uKXMReB6z9+WqryIYPZ/IeysTWDQEhLNcqYmCPcqIQJjhFOUCjE1YbyTrFe2fOcOMliniPVe07r2NHcv3GX36N8oKAsnDWBTbsOUlQn+/ziuaceVxtRo4XowtAEYbMEARjDiwmOEU4aG3/Yp9C15xACJf3GvSicgVDi39GBYqTzuhE33Kwcj6fzpgoCMIYNExwjnLSmDe8Ue++GXfxk426Kqn7oLRApNEbnhWXnzWDrW6XO6+Nlw22m03laQd0sQQDG8GKCw0ht2oiLkgJKnOB5gS8unOFrJGHnddqw20anWU7nWQX18eTzMWqDCQ4jNUmd9sKNlIK9tYO5AdA8G24lmuV03kyakdEc1FRwiMjngH8C8sAaVb09dF3c65cAR4Avq+qmpLEi8kHgfmAW8DrwZ6pauUepMWSSOu0lbaBRJ95m2HDT0Ayn8+NFUBuNQ80Eh4jkgVXAxUAPsFFEHlXVVwK3LQHOdP8sAn4ILKow9tvAv6vq7SLybff1LbVYw70bdrFq/avs/w+vG56ibr8CN6EWp1yTVOVatZ6T5VpOhEl/OJq/uujMEi0hjivmT/Md4wDfeXgz+w8fZdK4MSXOcjhmV99z8L2y7nKLZ0/kjYPv8ZDbV7zRN99mJotm1Lmzl9XPvMYrew5xtFCkv1Dk/T4nwCFIvf+dNvL/Q408t1H5HPOmnMgtSz5c1f/HREP/IKr2YJHzgdtU9U/c138DoKr/LXDPj4Bfqupa9/VW4EIcbSJyrHePqu4Vkcnu+DlJc2lra9OOjo5M8793wy6+8/DmTGOane9efk6s8IgqLHjbo10lJdlHt+RYe8NiP2rKuz8n4EboAnDjJ2dz9692+GOD44zho3NnL8t+9OuS35VxfJDPCT/56vmZ/x8TkU5VbQu/n6vazMqZCuwOvO5x30tzT9LYU1R1L4D798lRHy4iy0WkQ0Q69u3bl3nyT3TtzTym2Ulac9hO/kTX3rKS7OES3N79hcBGlBPYsvfdkrHBccbw0b79gAmN45RCUav6/1gtBUd5GnKo+GrCPWnGJqKqd6pqm6q2TZo0KctQAJbMm5x5TLOTtGbPTp4X/Kqqo/Klv6ag/bzk/rz4P4+OGGt298bAqaw73LMwaoFXTqha1NI53gNMD7yeBuxJec/ohLFvicjkgKnq7arO2sUz2ZiPwyGuquq6TT2RPo6o8utRY8OJhMbwsWBmK/d/9WPm4ziO5taMPo4WYBvwaeANYCNwjapuCdzzfwA340RVLQJ+oKoLk8aKyPeAAwHn+AdV9b8mzWUwPg7DMIyRTpyPo2Yah6oOiMjNwM9xQmrvdjf+G93rq4HHcYRGN0447leSxrqPvh34iYj8JbALuKpWazAMwzDKqZnG0UiYxmEYhpGd4YiqMgzDMI5DTHAYhmEYmTDBYRiGYWTCBIdhGIaRiRHhHBeRfcDOQQ4/Cdhfxek0A7bmkYGteWQwlDXPVNWyDOoRITiGgoh0REUVHM/YmkcGtuaRQS3WbKYqwzAMIxMmOAzDMIxMmOCozJ3DPYFhwNY8MrA1jwyqvmbzcRiGYRiZMI3DMAzDyIQJDsMwDCMTJjhcRORzIrJVRLrdcu3h6yIiP3Cvvywi84djntUkxZqvddf6soj8WkTOHY55VpNKaw7cd56IFETkC/WcXy1Is2YRuVBEfiMiW0TkmXrPsdqk+Lc9XkR+JiIvuWv+ynDMs1qIyN0i8raIdMVcr+7+paoj/g9O6fbXgNk4TaReAs4O3XMJ8AROd8LFwIbhnncd1vwxoNX9eclIWHPgvl/glP3/wnDPuw6/5wnAK8AM9/XJwz3vOqz5O8B/d3+eBLwDjB7uuQ9hzZ8E5gNdMderun+ZxuGwEOhW1e2q2gfcB1wWuucy4Mfq0A5McDsQNisV16yqv1bVXvdlO04nxmYmze8Z4K+BddSou2SdSbPma4CHVHUXgKo2+7rTrFmBcSIiwB/iCI6B+k6zeqjqszhriKOq+5cJDoepwO7A6x73vaz3NBNZ1/OXOCeWZqbimkVkKnA5sLqO86olaX7PZwGtIvJLEekUkevqNrvakGbN/wJ8GKcl9Wbga6parM/0hoWq7l+17DneTEjEe+E45TT3NBOp1yMiF+EIjo/XdEa1J82a7wBuUdWCcxhtetKsuQVYgNOq+QTgBRFpV9VttZ5cjUiz5j8BfgN8CjgdeEpEnlPVd2s8t+GiqvuXCQ6HHmB64PU0nJNI1nuaiVTrEZGPAGuAJap6oE5zqxVp1twG3OcKjZOAS0RkQFV/WpcZVp+0/7b3q+rvgd+LyLPAuUCzCo40a/4KcLs6DoBuEdkBfAh4sT5TrDtV3b/MVOWwEThTRE4TkdHAF4FHQ/c8ClznRicsBg6p6t56T7SKVFyziMwAHgL+vIlPn0EqrllVT1PVWao6C3gQ+KsmFhqQ7t/2I8AnRKRFRMYCi4Df1nme1STNmnfhaFiIyCnAHGB7XWdZX6q6f5nGAajqgIjcDPwcJyLjblXdIiI3utdX40TYXAJ0A0dwTixNS8o1rwAmAv/DPYEPaBNXFk255uOKNGtW1d+KyL8BLwNFYI2qRoZ1NgMpf89/B/yriGzGMePcoqpNW25dRNYCFwIniUgP8P8Ao6A2+5eVHDEMwzAyYaYqwzAMIxMmOAzDMIxMmOAwDMMwMmGCwzAMw8iECQ7DMAwjEyY4jBGHiNwmIt9yf14pIp9JuPdPReTshOs3JpXoEJFZInLN0GbsP+tCEflYwvXPiciLIvI7t9Lt/W4ujlcd9f8WkVdFZJuIrBeRuYGxr4vIZrda7JMicmo15mwcn5jgMEY0qrpCVZ9OuOVPgUjBISItbh7EjxPGz8IpIlgNLsSpWBw1l3nAPwNfUtUPqepHgXvczwe4yR17rqqeBfw34FER+UDgMRep6rlAB071WMOIxASHMSIQkVvd/gxP42QJe+//q7g9N0TkdhF5xe1X8I/u6f7zwPfcE/zpbiHA74rTs+JrIe3lDBF52j21bxKR04HbcbKyfyMi3wjN6UIReVZEHnY/d7WI5Nxrn3Of8ZKI/LuIzAJuBL7hPusToSXeAnxXVf2Mb1V91K2a6l3/a1U94l57Evg1cG3E1/UscMYgvmZjhGCZ48Zxj4gswCk78Uc4/+Y3AZ2hez6IUxX3Q6qqIjJBVQ+KyKPAY6r6oHsfwARVvcB9fVvgMffg1D962D3J54BvA99S1aUx01uIo9HsBP4NuMIVSncBn1TVHSLyQVV9R0RWA/+hqv8Y8Zy5QNT7iMiJwB+o6muhSx3uuDBLcSrGGkYkpnEYI4FPAA+r6hG3+mm4bhHAu8D7wBoRuQKnLEMc94ffEJFxwFRVfRhAVd/3TvcVeNHtG1EA1uJUIF4MPKuqO9xnJfVZKENEJrpayTZPG4q7ldIKqetF5DfAiTimLMOIxASHMVJIrK2jqgM4p/91OH6Nf0u4/fcR7w22Bnt4Xkr5hp6GLTgd4FDVA66P407gD11h+XsRmR0aMx+n85/HRar6UVW9TlUPZvx8YwRhgsMYCTwLXC4iJ7iawaXhG0TkD4Hxqvo48HXgo+6lw8C4Sh/gbs49IvKn7vPGuJVmK41f6FZxzQHLgOeBF4ALROQ091kfTDGXfwBuFZEPB94bG/j5e8APROQE95mfwdFu7q20NsMIY4LDOO5R1U045qXf4GgUz0XcNg54TEReBp4BPEf2fcB/EZH/7Tq7k/hz4P90n/Fr4FScirMDrpP7GxFjXsBxoHcBO3BMavuA5cBDIvISx0xjP8MRgGXOcVXdDHwN+LEbjvsrnA53nmD4Z5xy45tFZCvwt8BlqvpehTUZRhlWHdcwhgkRuZBkx7lhNCSmcRiGYRiZMI3DMAzDyIRpHIZhGEYmTHAYhmEYmTDBYRiGYWTCBIdhGIaRCRMchmEYRib+f3oRl7HaG3KIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABNrklEQVR4nO2deXxU1dn4v89MEgKWJeAGhlUQaoBqEiG8goKiiFKpoKWidaFuVd/W+rO1ta9oae2mba2KUqq0WHfFhVK1SIuKaFiCyiZIZA0gQgwBWbLMnN8fd+5wZ3Jn5k4yk4U8388nJHPvPXfOmYTz3GcXYwyKoiiK4hVfU09AURRFaVmo4FAURVGSQgWHoiiKkhQqOBRFUZSkUMGhKIqiJEVGU0+gMTj22GNNr169mnoaiqIoLYqSkpI9xpjjoo+3CsHRq1cvli9f3tTTUBRFaVGIyBa342qqUhRFUZJCBYeiKIqSFCo4FEVRlKRoFT4ORVFSS01NDWVlZRw+fLipp6KkgOzsbHJzc8nMzPR0vQoORVGSpqysjPbt29OrVy9EpKmnozQAYwzl5eWUlZXRu3dvT2PUVKUoStIcPnyYLl26qNA4ChARunTpkpT2qIJDUZoJJVsqmL6wlJItFU09FU+o0Dh6SPZ3qaYqRWkGlGyp4IrHi6muDZKV4ePp64oo6JnT1NNSFFdU41CUZkDxxnKqa4MEDdTUBineWN7UU1KagGuuuYaXXnqpqaeREBUcitIMKOrThawMH36BzAwfRX26NPWUlBQRCASaegopRwWHojQDCnrm8PR1Rdx+fv+j1kyVah/O5s2bGThwYPj1Aw88wL333svIkSO58847GTJkCKeccgqLFi0CYM2aNQwZMoTTTjuNwYMHs2HDBjZv3syAAQO47rrrGDhwIFdccQULFizgzDPPpF+/fixduhSAAwcOMGXKFM444wxOP/10XnvttfAcRowYQX5+Pvn5+bz//vsAvP3224waNYrJkyczaNAgAoEAP/7xjznjjDMYPHgwf/nLXwArounWW2/l1FNP5aKLLuKLL75IyWeTbtTHoSjNhIKeOUelwIDG9+HU1taydOlSXn/9dX7xi1+wYMECZsyYwQ9/+EOuuOIKqqurCQQC7Nq1i9LSUl588UVmzpzJGWecwTPPPMN7773H3Llz+fWvf82rr77KfffdxznnnMOsWbPYu3cvQ4YMYfTo0Rx//PG89dZbZGdns2HDBi6//PJwXbylS5eyevVqevfuzcyZM+nYsSPLli2jqqqKM888k/PPP58PP/yQ9evXs2rVKnbt2sWpp57KlClT0va5pAoVHIqipB03H046BceECRMAKCgoYPPmzQAMGzaM++67j7KyMiZMmEC/fv0A6N27N4MGDQIgLy+Pc889FxFh0KBB4bHz589n7ty5PPDAA4AVjrx161a6devGrbfeykcffYTf7+fTTz8Nz2HIkCHhvIj58+ezcuXKsP+isrKSDRs28O6773L55Zfj9/vp1q0b55xzTto+k1SigkNRlLRj+3BqaoMp8+FkZGQQDAbDr515CG3atAHA7/dTW1sLwOTJkxk6dCj/+te/GDNmDI8//jh9+vQJXwvg8/nCr30+X3isMYY5c+bQv3//iDnce++9nHDCCXz88ccEg0Gys7PD54455pjwz8YYHn74YcaMGRMx/vXXX2+RYc3q41AUJe2kw4dzwgkn8MUXX1BeXk5VVRXz5s2Le/3GjRvp06cPP/jBD7j44otZuXKl5/caM2YMDz/8MMYYAD788EPA0hy6du2Kz+fjH//4R0xH+JgxY3jssceoqakB4NNPP+XAgQOcddZZPPfccwQCAXbu3MnChQs9z6kpUY1DUZRGIdU+nMzMTKZOncrQoUPp3bs3AwYMiHv9888/z1NPPUVmZiYnnngiU6dOZd++fZ7e6+677+a2225j8ODBGGPo1asX8+bN4+abb2bixIm8+OKLjBo1KkLLcHLdddexefNm8vPzMcZw3HHH8eqrr3LJJZfw3//+l0GDBnHKKadw9tlnJ/05NAViS9CjmcLCQqONnBQldXzyySd8/etfb+ppKCnE7XcqIiXGmMLoa9VUpSiKoiSFCg5FURQlKVRwKIqiKEmhgkNRFEVJChUciqIoSlKo4FAURVGSQgWHoihKGvj73//Orbfe2tTTSAtpFRwicoGIrBeRUhH5qct5EZGHQudXikh+orEicpqIFIvIRyKyXESGpHMNiqKkgJUvwJ8Gwr2drO8rX2jqGdWbo7FMerKkTXCIiB+YDowFTgUuF5FToy4bC/QLfd0APOZh7O+BXxhjTgOmhl4ritJcWfkC/PMHULkNMNb3f/6gwcLjwIEDXHTRRXzjG99g4MCBPP/88/Tq1Yu77rqLYcOGUVhYyIoVKxgzZgwnn3wyM2bMCI+9//77wyXO77nnnvDxb33rWxQUFJCXl8fMmTPDx7/2ta+Fs9Q/+OADnnrqqXCJ9htvvDEsTP72t7+FM8AXL17coPU1Z9KpcQwBSo0xG40x1cBzwPioa8YDTxqLYqCTiHRNMNYAHUI/dwR2pHENiqI0lP9Mg5pDkcdqDlnHG8Cbb75Jt27d+Pjjj1m9ejUXXHABAN27d+eDDz5gxIgR4Y56xcXFTJ06FbAq1W7YsIGlS5fy0UcfUVJSwrvvvgvArFmzKCkpYfny5Tz00EOUl1udGA8cOMDAgQNZsmQJXbp04fnnn2fx4sXhqrhPP/00O3fu5J577mHx4sW89dZbrF27tkHra86ks1bVScA2x+syYKiHa05KMPY24N8i8gCW4PsftzcXkRuwtBh69OhRrwUoipICKsuSO+6RQYMGcccdd3DnnXcybtw4RowYAcDFF18cPv/VV1/Rvn172rdvT3Z2Nnv37mX+/PnMnz+f008/HYCvvvqKDRs2cNZZZ/HQQw/xyiuvALBt2zY2bNhAly5d8Pv9TJw4EYD//Oc/lJSUcMYZZwBw6NAhjj/+eJYsWcLIkSM57rjjAJg0aVJEmfWjiXQKDrdawdGFsWJdE2/s94EfGWPmiMi3gSeA0XUuNmYmMBOsWlVeJ60oSorpmBsyU7kcbwCnnHIKJSUlvP766/zsZz/j/PPPB4goix5dMr22thZjDD/72c+48cYbI+739ttvs2DBAj744APatWvHyJEjw6Xas7Oz8fv9gFUi/eqrr+Y3v/lNxPhXX321RZZIrw/pNFWVAd0dr3Opa1aKdU28sVcDL4d+fhHLrKUoSnPl3KmQ2TbyWGZb63gD2LFjB+3atePKK6/kjjvuYMWKFZ7GjRkzhlmzZvHVV18BsH37dr744gsqKyvJycmhXbt2rFu3juLiYvflnHsuL730UrjN65dffsmWLVsYOnQob7/9NuXl5dTU1PDiiy82aH3NmXRqHMuAfiLSG9gOfAeYHHXNXOBWEXkOyxRVaYzZKSK744zdAZwNvA2cA2xI4xoURWkog79tff/PNMs81THXEhr28XqyatUqfvzjH+Pz+cjMzOSxxx7j0ksvTTju/PPP55NPPmHYsGGA5fh+6qmnuOCCC5gxYwaDBw+mf//+FBUVuY4/9dRT+dWvfsX5559PMBgkMzOT6dOnU1RUxL333suwYcPo2rUr+fn5R20EVlrLqovIhcCDgB+YZYy5T0RuAjDGzBBLr3sEuAA4CFxrjFkea2zo+HDgz1hC7zBwszGmJN48tKy6oqQWLat+9JFMWfW0NnIyxrwOvB51bIbjZwPc4nVs6Ph7QEFqZ6ooiqJ4RTPHFUVRlKRQwaEoiqIkhQoORVEUJSlUcCiKoihJoYJDURRFSQoVHIqiHDU8+OCDHDx4MGXXJWLq1KksWLCgzvG3336bcePGJXWv2bNn069fP/r168fs2bPjXvvSSy8hIthpBlu2bKGgoIDTTjuNvLy8iIKOjzzyCH379kVE2LNnT1Jziokx5qj/KigoMIqipI61a9c29RRc6dmzp9m9e3fKrqsvCxcuNBdddJHn68vLy03v3r1NeXm5+fLLL03v3r3Nl19+6Xrtvn37zIgRI8zQoUPNsmXLjDHGVFVVmcOHDxtjjNm/f7/p2bOn2b59uzHGmBUrVphNmzYlXLPb7xRYblz2VNU4FEVpcbiVVH/ooYfYsWMHo0aNYtSoUQB8//vfp7CwkLy8vHD5dLfr5s+fz7Bhw8jPz+eyyy7jq6++YunSpUyYMAGA1157jbZt21JdXc3hw4fp06cPQLj6LljVegcMGMDw4cN5+eWXI+Y6ZcoUzjjjDE4//XRee+21Ouv597//zXnnnUfnzp3JycnhvPPO480333Rd+913381PfvITsrOzw8eysrLCdbmqqqoIBoPhc6effjq9evWq1+cci7QmACqKcvTzu6W/Y92X61J6zwGdB3DnkDtjnrdLqv/rX/8CoLKyko4dO/LHP/6RhQsXcuyxxwJw33330blzZwKBAOeeey4rV67kBz/4QcR1e/bs4Ve/+hULFizgmGOO4Xe/+x1//OMfueuuu/jwww8BWLRoEQMHDmTZsmXU1tYydGhkoe/Dhw9z/fXX89///pe+ffsyadKk8Ln77ruPc845h1mzZrF3716GDBnC6NGjOeaYY8LXbN++ne7dj5Tny83NZfv27XXW/eGHH7Jt2zbGjRvHAw88EHFu27ZtXHTRRZSWlnL//ffTrVs3rx930qjGoSgtiJItFUxfWErJloqmnkqTMmjQIBYsWMCdd97JokWL6Nixo+t1L7zwAvn5+Zx++umsWbPGtUdGcXExa9eu5cwzz+S0005j9uzZbNmyhYyMDPr27csnn3zC0qVLuf3223n33XdZtGhRuIS7zbp16+jduzf9+vVDRLjyyivD5+bPn89vf/tbTjvttHDF3a1bt0aMNy6ln6Ir7QaDQX70ox/xhz/8wXWt3bt3Z+XKlZSWljJ79mx27drl/uGlANU4FKWFULKlgiseL6a6NkhWho+nryuioGdOU08rrmaQLtxKqtuNmmw2bdrEAw88wLJly8jJyeGaa64Jl0l3YozhvPPO49lnn61zbsSIEbzxxhtkZmYyevRorrnmGgKBQJ2nfai70TvvP2fOHPr37x9zPbm5ubz99tvh12VlZYwcOTLimv3797N69erw8c8//5yLL76YuXPnUlh4pJxUt27dyMvLY9GiRZ6KPtYH1TgUpYVQvLGc6togQQM1tUGKN5Y39ZSajFgl1du3b8/+/fsB2LdvH8cccwwdO3Zk165dvPHGG+HxzuuKiopYvHgxpaWlABw8eDDcgOmss87iwQcfZNiwYRx33HGUl5ezbt068vLyIuYzYMAANm3axGeffQYQIYTGjBnDww8/HNYqbPOXkzFjxjB//nwqKiqoqKhg/vz5jBkzJuKajh07smfPHjZv3szmzZspKioKC42ysjIOHbK6LFZUVLB48eK4gqqhqMahKC2Eoj5dyMrwUVMbJDPDR1GfLk09pSbDraQ6wA033MDYsWPp2rUrCxcu5PTTTycvL48+ffpw5plnhsdHX/f3v/+dyy+/nKqqKgB+9atfccoppzB06FB27drFWWedBcDgwYM5/vjj62gX2dnZzJw5k4suuohjjz2W4cOHs3r1asByZt92220MHjwYYwy9evVi3rx5EeM7d+7M3XffHe4qOHXqVDp37hz+ubCwMNzZ0I1PPvmE//f//h8igjGGO+64g0GDBgFWMMDvf/97Pv/8cwYPHsyFF17I448/Xu/PHtJcVr25oGXVlZZOyZYKijeWk9Mui4qD1RT16dKkZiotq3700WzKqiuK0nCaq29Dab2oj0NRmjnq21CaGyo4FKWZY/s2/EKz8m20BjN3ayHZ36WaqhSlmVPQM4enryuieGN5k/s2bLKzsykvL6dLly4xw1CVloExhvLy8ohM9ESo4FCUFkBBz5xmITBscnNzKSsrY/fu3U09FSUFZGdnk5ub6/l6FRyKoiRNZmYmvXv3buppKE2E+jgURVGUpFDBoSiKoiSFCg5FaWFooUOlqVEfh6K0IDQZUGkOqMahKC0ITQZUmgMqOBSlBdFckwGV1oWaqhSlBdEckwGV1ocKDkVpYTS3ZECl9aGmKkVRFCUpVHAoiqIoSaGCQ1EURUkKFRyKoihKUqjgUJotmiGtKM0TjapSmiWaIZ0cdk9yDdFVGgMVHEqzxC1DWjdEd1TIKo2NmqqUZolmSHtHy5AojU1aBYeIXCAi60WkVER+6nJeROSh0PmVIpLvZayI/G/o3BoR+X0616A0DXaG9O3n99cn6ASokFUam7SZqkTED0wHzgPKgGUiMtcYs9Zx2VigX+hrKPAYMDTeWBEZBYwHBhtjqkTk+HStQWlaNEPaG1qGRGls0unjGAKUGmM2AojIc1gbvlNwjAeeNMYYoFhEOolIV6BXnLHfB35rjKkCMMZ8kcY1KEqLQIWs0pik01R1ErDN8bosdMzLNfHGngKMEJElIvKOiJzh9uYicoOILBeR5bt3727AMhRFURQn6RQc4nLMeLwm3tgMIAcoAn4MvCAida43xsw0xhQaYwqPO+4477NWlGaI5rQozYl0mqrKgO6O17nADo/XZMUZWwa8HDJvLRWRIHAsoGqFclSi4bZKcyOdGscyoJ+I9BaRLOA7wNyoa+YCV4Wiq4qASmPMzgRjXwXOARCRU7CEzJ40rkNpJrTWp24Nt1WaG2nTOIwxtSJyK/BvwA/MMsasEZGbQudnAK8DFwKlwEHg2nhjQ7eeBcwSkdVANXB1SPtQjmJa81O3HW5bUxvUcFulWZDWzHFjzOtYwsF5bIbjZwPc4nVs6Hg1cGVqZ6o0d5xP3VU1QeasKGs1gkPDbZXmhpYcUVoERX26kOETqgMGA7xUUsbE/NxWs4k6w221LpXS1KjgUFoEBT1zuKywO88s2YoBAoHWV7+qZEsFc1aU8VJJGbWB1meyU5oPWqtKaTFMyM+lTWbrLK1h+3ieXbJVHeVKk6Mah9JiOFpt/V5MT7aPx44CEVqf8FSaDwkFh4j0NsZsSnRMURqDo620htdoMWdkld8nXFbYnQmtyMejNC+8aBxzgPyoYy8BBamfjqIk5mhyDnuNFjtatS2lZRJTcIjIACAP6CgiExynOgDZ6Z6YorhxtOVz5LTLIhiyPxngxeXbYkaLHW3altJyiecc7w+MAzoB33R85QPXp31miuJCc8yibkhGe8XB6ojXtQHTLNakKPGIqXEYY14DXhORYcaYDxpxTooSk+aWRd1QDaioTxcy/EJtwFI7msOaFCURXnwcpSJyF1aPjPD1xpgp6ZqUosSiudn6U9Eb3Vb7/QL3fjOvydekKInwIjheAxYBC4BAeqejKIlpTrb+hmpAxRvLqQ0eKbUWbbpSlOaIF8HRzhhzZ9pnoigtkIZqQM3N9KYoXvAiOOaJyIWhooOKokTREA2ouZneFMUL8cJx93OkG99dIlIF1IReG2NMh8aZoqIc3djCwo6mUuGhNHfiRVW1b8yJKEprxY7MqqqxssKnjR/I5KE9mnpaihITLyVHorPGASqBLcaY2tRPSVFaF8Uby6mqsepQ1QYNU19bTf8T26vmoTRbvPg4HsVK+lsVej0I+BjoIiI3GWPmp2tyitLcSEe5k6I+XfD7JBxdFTSm1ZWMV1oWXsqqbwZON8YUGGMKgNOA1cBo4Pfpm5qiNC9sk9If5q/niseLU9b7vKBnDtPGDyTDJ/gEsuJEV7XWvutK88KLxjHA0e8bY8xaETndGLNRRNI4NUWJpKmLG6Yi2S8W/U9sz7fP6I5Anaq39rpz2mUxbd6ao6ZOl9Jy8SI41ovIY8BzodeTgE9FpA1WlJWipJ3mUNwwXTkX0WubkJ/res4nQtCYtAguRUkGL4LjGuBm4DasUNz3gDuwhMaodE1MUZx4LT+eLuyn/qnj8qg4WE1Ou6yUhc/G02Sc54LG4PcJfowmCypNSkLBYYw5BPwh9BXNVymfkaK4UNSnCxk+oTpgMMBLJWUxy4+nmmiNYOq4vDomIyApM5rT7GZrMtU1QUSEnHZZ4euc6wYQgUln9NAmTkqTEtM5LiIvhL6vEpGV0V+NN0VFsZ7qLyu0fAAAgUDjlVSP1gjeWL0z4vWcFWVJOc2jnewAU8fl4fNZpqhp89aE7xG9bhM0dOvUloKeOeooV5qMeBrHD0PfxzXGRBQlERPyc5mzoqzR6zpF+zbGDuzKss1fhl8LJOU0j9VTJJb/wm3dzyzZyt2vriJoINMvPHvDMNVAlEYjXub4ztD3LSLSE+hnjFkgIm3jjVOUdOD0MazeUUljxvO51ZPqf2L78GsgKYEWy8key/Fuv/+cFWUIsP7z/dz96ipC1iuqA6bRfT5K60aMMfEvELkeuAHobIw5WUT6ATOMMec2xgRTQWFhoVm+fHlTT0OpJ04fQ4ZPQITaQPMKSU02VNjt+nj3cH4GAmGhYXPF0B7cd8mgVC1HUQAQkRJjTGH0cS+awy3AEGAJgDFmg4gcn+L5KUpMIkw7AQNYDvLmFJKabIVct+vj3cP5GUST6ZeIEF5FSTdeMserjDHh7jIikoFVNVdRGgXbtOMXa5PMtH9uRSGp9mcQbaL7Rm5HnlP/htLIeNE43gm1jm0rIudh5XT8M73TUhQLN99GXreOVBysbtQM8t8t/R0Adw5pmp5mTj/HSyVlBAKWL2SqtppVmgAvguOnwPewihzeCLwOPJ7OSSkKNC/fxrov1zXae8XCNmVNzM/Vxk9Kk+JFcIwEnjbG/DXNc1GUCFqCb6Mp0MZPSlPjteTIDBEpBxaFvt4zxmjWkZJWnGGr/pDGYZtojgbfRn2LNjaHul1K68ZLyZGrAESkG3ApMB3o5mWsojSE6PwJSK6sRzppaKXeZLr+Rb9XOqv0KooXvHQAvBIYgdXAaQ/wCJbWoShpJzpEtTlskKl44k/U9c8WFvsP1fD4e5sIGhN+r3RV6VUUr3jRGh4EPgNmAAuNMZvTOSGl9ZHM03uiJLnG0EheXlEW3vTr+8Qfr+ufUxtxxr1X1VjvdcuovnUy2RWlMfFiqjpWRPKAs4D7Qpnj640x3000VkQuAP4M+IHHjTG/jTovofMXAgeBa4wxKzyOvQO4HzjOGLMn4UqVZkkyT+/xrm0su3/JlgpeXL4tvKH7/fV74i/omcN1w3szc9FGjIns+ucUTE4MhCvnJptwqCipJGECoIh0AHoAPYFeQEcg6GGcH8sfMhY4FbhcRE6Numws0C/0dQPwmJexItIdOA/YmmgeSvMmVsG/ZK9N5j4Nna+tJQhwaUH9yps/s2Qrj7+3CWPA7xOmjssLaxtOweREgIqD1S5nFKVx8ZI5/h7wTWAlMMkY098Yc7WHcUOAUmPMxlDm+XPA+KhrxgNPGotioJOIdPUw9k/AT9AM9iYlFWW9I7LCE9jr412bzH0agvN92mT6mBjVrc/L51GypYKpr62mNmiFFweNCQuEaMF0Rq+ccC/yNpne1+VlLlqWXakvXkxVg+t575OAbY7XZcBQD9ecFG+siFwMbDfGfKw9z5uOVJmG3CrP1ufaZO7TEGK9T6zPw83vUryxnICj6JRPJCwQokOQTzmhPZecnptUpryX342G9CoNIZ0htW67erSGEOsa1+Mi0g74OXB+wjcXuQHL/EWPHu5hjkr9SWVIaCJ7ffTmG88pfsuovgnHNxS3OcQylbltzkV9utAm0+r45wuF4tr3iy4t8uzSrUlv7F5+NxrSqzSEdAqOMqC743UusMPjNVkxjp8M9AZsbSMXWCEiQ4wxnztvbIyZCcwEq6x6QxejRNJYIaGJnowbej5VRH8eOe2yeHDBp66bcyLtqKBnDi+H+nvEityKJwy9/G40pFdpCOkUHMuAfiLSG9gOfAeYHHXNXOBWEXkOyxRVaYzZKSK73cYaY9YA4ZLuIrIZKNSoqsansUxDsZ6M7Y1zx95DcZ+c0/lkHb15259HTrssps1bE46M8rn4XeJpWYkitxJFl9lFIeOZtxrr96ccnXhJADwOuB4roip8vTFmSrxxxphaEbkV+DdWSO0sY8waEbkpdH4GVsHEC4FSrHDca+ONTXp1SlppjJBQtyfj6OKHGX5fzFIk6XqyjrV5F/TMYfrCUqpD2oIPOLPvsdw2+pSkTE3xIrfiCdNktCsN6VXqixeN4zWsTPEFQCCZmxtjXscSDs5jMxw/G6xGUZ7GulzTK5n5KC0Ptydje2MOGggEDZOGdOekTm1jmn3S8WQdT5OJFla3jT4FgOkLS+POwdYWctplRYyfGNWkyc0sNn1haULtS1FShRfB0c4Y0zRNCBSFuk/G0RvnxPz4uRRenqyTdaDH02QKeuYwdVweb6zeydiBXYEjTnKfWP1EJp3RI6I2VbS2EM/U5GYW86J9KUqq8CI45onIhSENQFGanFRrEfVxoMebQ8mWivBmvmzzl0zMzw1rAkEDH5dV8nHZKgD6n9je1VdTcbDaNULM+f7Jal+Kkiq8CI4fAneJSBVQg2V2NcaYDmmdmaKEcNMGUmmfr68DPdYcou9nsEqKHK6JLLjw/LKtrN+1v4624PcJO/YeomRLRcJ5JKt9KUoq8JIA2L4xJqIobjRGOG2qHehum/nE/FxmvPMZb63dFb7uhA7ZrNpeGaEtAOH8jTkryjytd0J+LhL6rkJDaQxiCg4RGWCMWSci+W7n7WKEipJOGiNRraGmL6dT2/ZLuN3vr1cV8sySrWHfR/8T2/Puht0RAqZ4Yzm1AW/rje7pkdetowoOpVGIp3HcjpV5/QeXcwY4Jy0zUhQHjZWoVl/T1zNLtjL1tdUEQnWnfEJYM3LzUUweGukUdzrR7ff3ut5EPT0UJV3EFBzGmBtC30c13nQUJZLmnKjmLFZok4xmFO1Etzf9eE535/F4PT0UJZ14SQDMBm4GhmNpGouAGcaYw2mem6IA9dMGGqOp08sryiKEBhwpsvbxtr0Jndu2Ge7vGfcxwrcG/mYdLwh98c6Ra9d3n8Skz74V0QmwoGcO08YPZOprq8PHNQRXaQy8RFU9CewHHg69vhz4B3BZuialKNEk2yWwoQ71RO9nlwWx8fuEbw7uyj8/3kHAwPy1u3j70908e33s9772w+9wc9YGABIVej5l6/NsyHyeJwOj+UXtlLBmMXloj3BIr5fkwuamtSktEy+Co78x5huO1wtF5ON0TUhRIHKjA/cqs27XFvTMqbdD3enktmtN+UPVa6OJLgsy6Qwrf8KpgMR879kXw6Z3aGcP9oAtWK7yL2C0r4TP+3wYPuelurCWUFdSiRfB8aGIFIUaLSEiQ4HF6Z2W0pqJ3uicCXTRm7HbphjPoR7rydt5H59I2NltO51PG1JL++wj/13cQm4BMv1CdcCSHnWc2/Nuh+VPNOizEYFuUsFJLw6DO9Z5GqMl1JVUEy8cdxWWTyMTuEpEtoZe9wTWNs70lNZIrAQ6N0HgtineMqpvUs2Wou8TNMZ6wg9pD4GgoWzvQXI7tQu/bywn9rM3DGPOirK6eRWPDIU93jb6RAjAVzute966JOH1WkJdSTXxNI5xjTYLpcWTShu6vdFV1wQREQZ26xjOcYi+f6yCf25NnRIVJszwHdEWfAKIEAxpHvsO1fDJ4X0RDm83E5Gr2eiBAdZGn2r2rKsjPGJl2TfXyDSlZRIvHHdLY05Eabmk2oZuFwm0o4WmzVsTMy8iVsE/t3kkKkx4WWF3nlmy1VI0DEwa0p1tXx7kvQ17MAZINtx15Qvw8vX1/hw84RAe8X4PWkJdSSW+pp6A0vKJ1Ta1IVQcrCZoDEED1TVBpv1zDT9/ZRUlWyrqXFvQM4dbRvWl4mB13HnYQub28/u7CrcJ+bm0yfThDzVempify9iBXcMNlQyQ0y4LsITl9IWllGyp4JklW/nuE0t4ZsnWIzebd3v9hEbvs+HeSnb1v5KgwRJYidizDubdnpbfg6K4kc4OgEoLJZUlxuuL01wVxK4oW8mLJWXc+808Vu+orONH8DKPeE/ebiad4o3lltkKyzFdcbA6ypEOtaHahYs2WI0oJ6+7FTa94/oe0diCYV/XM+l405EC1C+d+CMe+PhC7s2YxVX+BQnDdVn+BEXX3k2GT6gJGPw+UV+GkjZUcCgRpLrEeH3nYLc/fWP1TstUFDpXXRvk7ldXEXJF8GJJWThXItpsZT9xxwvdTYQtjKx9W8L3dZZJdzLujSIMBzxF2RoDO0wnzqp5lNv794/oaFbUpwttMn38omYKPhGu9L+V8J5937gC5H8BkzgxRFEagAoOJYJUlxhPFreGRks2loed1n6f5bC2iZ6j/d1N+EW3nL2ssHtYYynZUsGcFWW8VFJGbSBy3NRxefxqhRVmNW3eGqaOywtrNhLSOC72vceDGY8ieEvNMMBiM5Cra+6K0I6cgu3p64qYs6KMddzDF4dP4IT1T8W9Z4fPF3Oh+QavMpxAQMNulfShgkOJoKlDN6MFV8XBap69YRgz3vmML/YdZlifLsxavCl2roTLPewN1Hm8OmB4ZolVunzquLxwwp8tkpzjKg5WAwbjmJNTw+r62nfo+mWx11w+QJAJM2nb8Txud2g/0YJtZP/jefvT3dQGgszJGMc7pxJXeAjw+4zH+GdwuIbdKmlFBYcSQVOHbjoFl93QaP3n+1m0YTfVtUHW79rPvRcPdPVxuN3DuYHax20BYbAEwRurd1Jde0RoCNQZ9+h6S+Owj4c1rN/0gKpKT2szBqoy2vPE8Lcp6tiljpYWLdjmO3p31NQGeenEH3FL9ba4/pNMMbzS62VqLnhAtQ0lbajgUOrQWKGb8XIObLPRs0u34hMJR1jZT/y/vmRQ3PlHCz+n32T1jkpeKikL9+YeO7BrhDnM5xOmjsuLmNPXT+zAvsM1/ObCkM8nySxw259x9qHHCM5fHzdc2Kn5QJQgGzUXpnWBYK3r+wgweOdL0LNhGeqKEg8VHEqTkCjnwNnQCGPw+QRxPPEnwr7XjHc+Y9Pur9jy5UECwSOVZSfm5zJnRRl79lexZkclI/sfH37CDwYNq3dEahHtszNon52RtJaBNX0WBfOYEvi/CAEY7YOw/SlO5z/UFWR867HEob7zbodxf/Q8R0VJBhUcSpOQyAkfbW6aOi4v3F3PWUIklkmtZEsFk/7yfjhU1sZ+r6I+XXhp+TaH0/3INQZ4cfm2cP2p4o3l7D9cS/sDW+Dejp7XaIx1r9trb+Zg/4lM63880+aties/qjhYHaFt2Dey/CwhBn8bPnwqrsnKLH+CR4+5WTPFlbSggkNpEhI54RP5WuJpLCVbKnhwwad1hAaAyJGQ2hrHY30g6tragGHGO5+xcN0XTPU9QfvclUmtz2CZps6sfhSAjHVfcOPZJyf0H0Xnr/jEPQCAq+fGF2IGBv33Kq74791aDVdJOSo4lCbBixPeWYDQ+do+ZmssVTVB5qwoi4hMqqqpKzV8QriEydRxeRGVbDP8VkxUra2B+IXvlv6QmZmrAVjI8Umtb9+JZ3L21luxKyXa3fluGdU37iYenYsSrWVFUPg9zPInXKO5RGCEbw0X1L5L8cZ+nnqYaC0rxSsqOJQmoyF9JJxFCQ3wwvJt7Nlfxa59h8MRUj6ga6dsdlQexjiS9Zxhvs5KtgBzVpRxTelt9DuwPOk8OjtSa/NZD9LnnGuZFupHnmx3Ps/BCeP+iKyYHdtRLvCrjCdY3+cncW+j/TqUZFHBoTRbEvlBvt61Ax+XWU7q2qjwVQEyMnyM7H88zy7d6hpqG7FBh8qeF2Bt/mJf7BFjYJ05ibHV95P1Xx/PnlwR0Z0vOpM9ZU/4CRzlx0gVBZVvAd+OeY3261CSRQWH0myI3kxj+UFKtlTwzBMP8ByPkd0mEP+mH8Ovshyv7bpTf4s9JJliHbaW8aOam3ktOByI3HzdMtnthMP6POHXETiDvw2v3QqBqthr+edt1nUx7tPUSZ9Ky0MFh9IsiGUuqeMHmX0x+ZveIV+SMCOlqWyTAdYHcxlb/fuISKhEzabshMP6tLZ1NSmNfwTz8vWxl1lzIOF9tF+HkgxaVl2JibN0eLqJVRLcLplesOqXVhTRpncI9VhqMoyBWuPjxe5TubDm92F/yjdyO3LF0B7hoos29hO9Xa597MCuEa+9PuHH+oxKOp7HAZMdf/C8213vczhUsh5I6LhXFBvVOBRXUu0wTWTTj2suSTLhLp0EDfwjMJppgSlM6tyDrIyyI7km38xzXZub5mT7PpJ5wncrx2J/rv+oncKfMh6NLVCXPxFOCIzudvhxWSWX/7W4jsBTlFiI8dQppmVTWFholi9f3tTTaFFMX1jKH+avJ2jAL3D7+f1dO/BF4yYgvAqhOmMbo4NeAkzIU35Nbk+2ZA9gx7prw6VKnr6uCKBRTTzRVXydxRBX+r9LttTEHtz7bCv/A/j5K6t42tF4SoA7xnj7HSutBxEpMcYURh9XjUNxpT4O01gCIlbORTQRUU6zL/bcDCnVmPA/VqmQN/P/ws6q+zg2O5M/XF/XF9CYT+nR5ViqA4a31u4i0y/M7fEzLts2Laavw2x6h7eee5guw64kr1tk8mCGXxs/Kd5RwaG44iVzO/pcrLDO6JyLl0rKmOioalvnXmkQGk5hAIDE9pnvO/FMhm7/37BJiJIyMk46iITsQE39VO5W5TcQNOzuMx7Z8bu4EVYFa3/L0JUnc86A40MJkdbxywq7q5lK8YwKDiVM9AYeKxEtlmYRS0sp6JnDZYXdeWaJlU/hbDIUfa8lJz1Mx88XJz/5wu/FLOpXsqWCy2d+ELbpZ2X44trzOwJPhz6LHXsP8ezSrfhDkifdOQ5e8juiKwjbprOcdlnM73c35637v5hCsbN8RW3Q8J9PduH3CSZg8PsFCb23Cg/FCyo4FCA5Z3gszSKeljIhVI02Wqi8vKIs/OQ8k1/S4fM13id97AC4dUnCy4o3llMbShsX4NKCuj08orHXY/sURKw6V+k05yTzO7DnNzE/N5xgaOWG9GFtVmZcX8fFvvf4Z3A4BC0NsDZgeHap1dRKs8YVL6Q1HFdELhCR9SJSKiI/dTkvIvJQ6PxKEclPNFZE7heRdaHrXxGRTulcQ2vBTRjECseNDi91bqbh8NmozccWKref3z+ileuLy7dhgF9kzGKEb43HlAuBCX+l5JtvegoXtufrw2o9OzDKvh8Pe965Oe3o1fkYijeW88ySrXHft75hzMUby6mqCfkuao6E2yaa3y2j+lJxsDr8+/tp7fV1K+yGEIHfZf7F0jYcx6NDfBUlHmnTOETED0wHzgPKgGUiMtcYs9Zx2VigX+hrKPAYMDTB2LeAnxljakXkd8DPgDvTtY7WQrSZKaddVtx+GckkjDnNL07/gK0JXOx7j6v8C7zlZnytK9yxLumn86nj8sJ1o6bNW0P/E9t7frIu6JlDhw8z+eTzfTywYr2VtyG4vq9bz/S4hQod5LTLCm/mwdBrrxT16UKG3/r9vS5n8UcejSmEsyXA7MItfO/DXuEqvIIlVNVBrnghnaaqIUCpMWYjgIg8B4wHnIJjPPCksWKCi0Wkk4h0BXrFGmuMme8YXwxcmsY1tBqihUGi+kVeC/ElKlSYleHjT77HvAkNh2nKS32lsJkp9DpWE6VYIcTOY/sO12BCph3rXu7vG9H+tSYYUeQwkRmo4mB12GHtEyJ7cHghFFofDAb5d/ZFXHDoX66fqwBnrp7K09d9ypwVZby4fJtVFbgpsyqVFkU6BcdJwDbH6zIsrSLRNSd5HAswBXje7c1F5AbgBoAePXokM+9WiXOjBNi+9xAZfl/Y8er2JOrFkRtvgy/omcOqNjfgq/WQSxTlz0gULhztEM/wS3g9IvDKijI+3raXPscew+PvbYrY3IE6wi7T54vdztWBc14S1fJ2zoqy+idBJuDlFWXUhKLWaoPw/b1XsLHNv2Kb/oLVFFS+xct8ndrQOGfQgqLEI52Cw+1vNnqHiHVNwrEi8nOgFnja7c2NMTOBmWAlACaabGvGqRVk+KwiUHZy2XeG9GBCfl1nsldTUaKM8MzafXHnZgBxJK7ZOCOL3P5Y6jRqChi+PbQ7u/dX8dbaXZTuPkDp7sgaTtUOG3+0sKsJBiMeyHt2accNZ50c05fjdFjbYb32k31mjKiuZE2ANk5fkZN/BEZzVcaCmMIj8MrNvFj9ZHic32+ZKKcvLNWaVUpc0ik4yoDujte5wA6P12TFGysiVwPjgHNNa0h9TzMRWkHAYBtkAkFDt05tXcuAJ1OKe0J+bkTPi+kLS7n2w+/QLkEZEWNgsRlI25GzKYhxzcsryqgOPc1Hm8GcjZp8Iaf4G6t3xnw/nyNqKrq0R6bPh4jgw/I/bP3yYExfidOMZ5cW+Xjb3nDZ9+raIC97SYL0iDNqzMk9tVO4KmNB7PWaGi5kEa8yHAHOPuW4elftVVoX6YyqWgb0E5HeIpIFfAeYG3XNXOCqUHRVEVBpjNkZb6yIXIDlDL/YGHMwjfNvNTijjpzbj99vaQjPLNnKpL98wB/mr+eKx4sp2VIRN7LKxtZKnguFeq7/fD9XPF5M+//cSdvKDXHnZIzVevXqmrtiRvrELPoXEnL3XjyQ8049wYogCjnF87p2cL2X3ydMGz8wIqx40pAeIMKzS7ey+csD9Op8DGf2Ozbsh6iuCfLggk/jRlfZwvbY9m0izq3eXpmy4pHO30WWX8gMdTP0CfyjdjSxHq0Eq9GTX6BNpo/j27dx/TwVJZq0aRyhqKdbgX8DfmCWMWaNiNwUOj8DeB24ECgFDgLXxhsbuvUjQBvgrVAmb7Ex5qZ0raM1YG+UDy74lMWle8LZxJcWWBrC1NdWh59obXPOLaP6JjSruJUTv8s8zncTRFAZAxWmHWfVPBrX1u9mBnsmquvexPxcjMPP0L5tJr++ZBDPL9tKmwwfK7btJRAw+MXSDuDIhi8QLu1hjKEmGOS20aewbPOX4WikxaV7WLb5S0/RVRk+wn3QV5ZVcvnMD3j2hmEpKR7pjN5a//l+pr62mkDQMLV2Clf6Y5urjpEqbj+/Pzntsli9ozKhX0tRIM0JgMaY17GEg/PYDMfPBrjF69jQca3ClgYKeuaEN0V7I7aTywIOM4jTnOMsNWLjbMUavbH/8PAMCjwJjbYUBZ7gO0O6k9eto2vPcfu1U3hBXSFnoI5wKeiZw+ShPZi+sJTlWyowWCa6OSvKACL8PUcc6kKH7ExXIZsouqqmNsjqHZX4fD4IWpLDYNWZilW3ywvxaoMFzZEIsH8ERscNd7708z9x9tpx4TXH8mspio1mjith3Jyz6z/fj98nBIImwpzjVqU1iJWFDPD88m1MKuwefhI+reItCj5+OWHEZxDIr34Cv1gbXyKbu9MnMH1haR0hNzE/NywAbeFiO3/damgJRxzjgaBh0pDunNSpLQsrO9A+OyP8ntFCNl50VWaGL6y9RNOQANh4tcGyMnxhreie2ilc5Xf3dQhw3PqnqK69MLxm26+lKLFQwaFE4NyIS7ZUMG3emgihMXloj/CTrl0qBAiHgtrUBgzPLNlKm0xrwz/9bz9LKDQM8BNza9hv4tzEvXTKK+rThTaZ1oYpAucMOD5iTW5P6M4aWjW1Qb7YX0WGT6gJWGu2izEufzPyv0qiCKjo6KqF679ARJCQJmCH89oBA/UhXm2wp68rYto/14R7sh8gm69x2PU+AkzL/Bv31FyrJirFEyo4lDCxIqcMlo1/9Y5Kpi8sZcfeQ+HjENoE/ZEaBxDejL8+exASswjGEaT32UweeQd9HNqBW30rt3nbJrKp4/JYvaOSl0rKWPDJLt7dsNu1vLstiCbk5/Li8m1hrWPh+i+OJAxiRW3FIlEElH3OmU/i9wnXD+9N+7aZDQ55jSe8CnrmMPCkjmHBcVfNFP6c5Z5NLsBk31v8u++PGTuwq2obSkJUcCiAu708uuOc0yxl2/79PuGyQssXsXpHJaW79rN8SwW2xWhZ5nW0DR6Ma5IxwKEO/Wh39VwKiPRlJCrtPmdFGS/Ymc9Y/oyRpxxHTUiwuZlwov0dkZV7TXhOtQHD00u28vyybfQ/rYrjO0RGRnkhOp8kGDS0b5uZstLs8YTXhPxcXiyxBO+bvrMwvieQYIyS6wbO2/R7pm2+LhwkoD3IlVio4FCAyCq1drOlX18yKLxx2+XF7VyP0acez2ndO4W1AFvo+Bz2qMVZN5PjSyA0DKwzJ3FJxS95OlTW2628ezRu5jKwTFtvrd0VkdQWbcKJ3hCdlXv9oQTIGodGVRs0bCo/QNssf9Kfa3Q+SWOaggp65vCso/GUr/IRzMvXu2sdAlf6F3BP1RRmvPMZizbs1nwOJSYqOJQ6mcfRzZaclWxtk847n+7mprOtzOnpC0vDJiCMwecTFmV+n26yN6HQ2GE6Mbb6fnxYORFjB3b1lITmNKPVuW/oux1SnKjGllt01pwVZTy/bJvD2W7YdzhOW9YYFPTM4dkbhkVEmzXmJhzpszqPPBO75LoA3/S9x7/WjYgIYdYyJEo0KjgU18zj6LpFdU06sU1AH2VdT5vA/oRCo8K05czqRwHCOREffFYeru8Ur82sM3JIxJpfVW2QlWWVYcHhE+I2KIqn2RT0zGFgt47hnBA7HLc+1CcbPBli1Qxz81k9WXM9D2Y+6l78UOB3GX9hXvVwfD5BMOosV1xRwaHUCd/0xcgGj9WMyfnEfuO7Z5IRcI/esbGFRn71E/h9MLBbR1ZtrwxrLPamZoDnl21jYLeOTB4aWagyulT6yu2VTB2Xx/pda8LCxOeTmA2KvNTamjy0R7hkiDMc1x7v3JTt13YiXTztwktxSK/EWkcsn9WfZQR/SlBy/ZKs9ym46EbP5eCV1ocKDqVO6GisDSP6OmdiXkHlWxS8c72n9wsaK1cDYNIZPZiYn8sVjxeHBdJZ/Y4L13UKBA1TX1vtWhOq4mB1RPXZioPV7j4ZDwl69lqiN3RbW7DDcaPzV+ys8Gnz1tTxt7xYUlanmGEyfUS8ECuXw+34LaP68uwNw3jjmQu58PDrMbWO++VRfEPvq/eclKMfFRwKEN+c4vaEXN8+4cbAj2pvBgiXBHHzMfx33Rdh81nQuPf6jhUl5UxQjJegZzc+sqvCJtrQ9x+urZu/Eiql4uZvcRNYziCEVPgPYuVyxMvxKJn8KGZWr5hah48gzLs9Zg93RVHBocSlZEsFl//V2lD9PuGX4wdGtCn9j9xEh8/3erqXMVb5i3+Z4Zx/6gmM7H98pNbi2ECnjR8YUXPKzc6eKI8hYYlyu/qfMazZUZkw2XDf4Zq6+SsZPsYO7BpRv8omWmBFByE4I77qS6x1Jvpsdg24kuPXPxXbD7X8CRUcSkxUcChxscuWg2U2+vmrq7hxRB8uyXyf38sj+EhcNsMYQIRNZ/+J/TKCFxwhvFU1wYisdBunfyGenT2ephQvlPfBBZ9SG+roFwh9T9REqUN2ZkRey2WF3cN+DHuuTh9HdJ0tZxCCW8RXfYm1znifzQmXT4d7n4p/Y9U6lBio4FDiEm1+MQbGF09kgG+7pzpLxsABk8nskcXcMqpvuKLl9IWlYZNNbQw/RjqikezqubawsAMBomtaub1v++yMuE/xiXwZ0eajiQ0oN5Isrg75wu9hlj+hWoeSNCo4lLhMzM/lhWVbw+XAV2R9jxw5lJTQKDT/4GkXH4PfJ+En8EDQ8OCCT7lt9Clpi+Ip2VIRUT1XgDP7Hhvxnone26swi+Wcrk+Hv4YSM/Jq0N0MXvZ3MiXgOs4AolqH4kI6GzkpLZiSLRVMX1gKwPM3/g+zjnuOjW0mW0LDY0nX6rYnMHtUccyqttPGDyTDJwjWJrW4dE+4UVRD5hxrfHSJeBE812baf7iW7XsPeZ6bszmWiJDTLguw1n3LqL6NGuLqFGJ2box9/Me1N8Zt9MTyJxptnkrLQQWHUgf7CdXu+Df4H4M5Z/9cQtU4vHHsANr89NO4m+TkoT14/sZhDE+iq14s3LoURq9p+95D4aq7YGlE0+atSfheJVsq+OTzfZRVHPQs2Ow8E1+oJP3U11bzzJKtrveOJ+xSgV0+Ho5UBbC7OL7pOytx+clHhqZtbkrLRAWHUgf7CXWcvMda33fIqN3naZwx1sa0suullHzzTU9jCnpavS3sp3M7gzwZzcNpggqaI10KneftFrYYw+DcjviEiJDYeBRvLMcYg0mypWrFweqwL8X24zjXFC2g0yU87Kx/W2A6s/6fvq6I1d0ujS889qyDlS+kZW5Ky0QFh1KHoj5deDLz1/w589Fw2Y5E2HWn+lU/y7c2T/C8ETpbnzr7eTtNKomI16XQPu9szpR3UseE/dKdFPXpYvXS8Hi9c5zfd+TTs/NR3OaV7h7fE/JzaZNZd80FPXMYfOMTiC+Bu/Nl7c6sHEGd40odCv55Aca3znN3OmNgUTCPKYH/i8jkjpWNbRPttL1mWC/eL91j3ZPIQovxcDZw8jm6FDrPR0cz2RFU0Rnwrp9Hzxy+fmIH9h2u4TcXes/0tv04sfJRYiXp1Zd4pUzckiztTogFPXPYOPwBer9zWxxTZEDDc5UwKjiUSB4YAF/tTKKlqZWfsUpGcN2hGh5/bxOEiuPtP1TDpL98EN40o53kzifu6pogj7+3CUfrijqFFmORKNkv3nmv5T/aZ2fQPjsjaad2vHwUT0mKcXAKCi9rcWbVR19bLCM4x5zEALbHFh4anquEUMGhWKx8gWCoV4NnoXHsALh1CX2AotBmFAgafAKDT+rIX9/bFDYhVbtkYzufuEUkMuKJ5MxCicJk3c7HqvOUauqTpJiI6M1/Yn5uzLXE6uzovDanXRZjq+9nU5vJ8d/4kaFw65Kk56scXajgUGDe7ZjlTyTh8BKYMBMGfzt8xNkfI2Bg6ea6/g07JNXG+cS9/1ANM97dGD533qkncGOo30e6SLWpqDGJ3vxjZb6XbKng8pkfUBMwZPqFZ28Y5rru4o3l+CR+b3LAcpTPvhiunts4C1WaJSo4WjmVMy6kw+eLvTnAAflaV0ou+8B6gg05v+0n1qwMX50KseGxodBXIKL6ri0YHlzwaTifwyfwje6d0p7r0FBTUVMSz2/jXMucFWXh7oPVAVOns6Pz2gyf8POaKTH7dYTZ9I4VZeV4cFBaFyo4WjFVvz2FDod2ecrNMAYOdezHJxPfCptIMkKJHc7y4qt3VEZ1zguNx/JjOB3FT19XBBBRcdYnxCxqmA7SUdakMYhX3NDODSnq06XOA4Hg3ktkzooyDDA3OJzC4Aau9L0V/+/i5ZtUcLRiVHC0Vn7Tg6yqSs9CY2eXIrr94N8UO9rE1gQMYML5EBUHq/n1JYMiOufZwiUQsPwYblFXtonLR90SILFIZTMkr+w/XBsRidTUuAk9t0g1v08IBq2AhbxuHSPOu/USmVpzLZe1eZts4rXKDajJqhWjgqO1sfIFeNlquOSlqq0R4aUed3Py6Cl0I9JE4ncIBb9P2BEqyREdSQRHzFnT5q2pY4d3mlzGDuyaMDw2enOcOi6vQd3qvAih/Ydr+eTzfaxYsj4lDZjShVukWjBo8PuEe7+ZF1ES362XiACZfuGu4I38wfdIYpOVhui2SlRwtCZmX2z9Z/eA3d51SM0sgqWGrM1Hak5F5wPYHfGi27Q6o3oA+p/Y3tW84uwqOG3emoThsdGbY7T5K5kN3WtHvn2HazBR2lJzFBx2eZGagNWCN2gsjdAYExau0YJ62eYv65SKX//5QJ6atz6xycquZaXCo1WhgqO18MhQKyLGA3YW+Nm1j9UxLTkFgi0AhCORPdGbql3G3Lmx3zKqb8T72fec7jSDxdmco8N43eboFa8huR2yMxERz9nmTYpYYQbiE/whjdDZITFaeLvlmRRvLOcPtddyWVYikxUqPFohKjhaA6GkPi8YYF/XM3m1/0NMc5iWbFPUM0u2snpHZbjndoZPCHKkb4ezq110GXO3XA4nXsNjnZtfLPOXV7y+Z/vsDL5+YgdG9e/fbHwcbhRvLKc2YAlxEzRcOqQ7J3Vq69pH3cbNV2J/Lj+tvZ4/ZSSIsgIVHq0MFRxHMw5/hheMgS8GXMkJl08PN1zqf2L7sCnqmSVbrZBcjgiKGkeqt2B1tQOrnMWOvYfi1pCKJpnwWOdm56VTYCres312Rh1tqbnhFqZb34x0y3fUjy8+38cJ6xN0CwQVHq0IFRxHK0mapoIIPw7ewhX/cwcnOM6FW54GjjhQox2pOMwhA7t15PK/Fltail/IDJuU4LrhvVPWKKmhY1I5vjmRijIm0T6fE0ZNh9nbvPnHlj8B5aUabXWUo4LjaCMJBzhYQmCH6cSZ1Y/iFzjZxZRkP8VW1wQJYuVaZDgcqXAkaur5ZVvDPcprA4YhvTqxYutegsbw9w82c17eiUfNJg1NExaciIYIwpg+n6vnen8Y2fQO/CIHLvmL5nocpajgOFpIUmDYHOrQj3MrfolfYtv4o30Kq3dUIliluu0Nav3n+yP8GTZVtUFX53Vz3HCTxWtEVksirs/n1iXe/WUmaJlJ3/2D1rY6ClHB0ZJZ+QK8egsEq+s3vvfZtLt6Lk972MTtp9hnlmzlhWXbCBoTDr0FXIVGll+YdEYP1u+KdF4fLRtuYxVJTAVeBXVCU9cd65IKtmDPOri3I/Q+W81XRxEqOFoa825PQR/oyCKFXk0bsaKkgAgnuF/gO0N6hDWSaOe117Db5k5LKZKYrKBO+Pdwx7qkfGiApQ3f29H62d8Gxj+iZqwWjAqO5sjKF0Id1wKpv3ebjvCzur2vvRCv0150I6XJQ3uEr3PmBkDL2XAT0VKKJKZFM0rGbBVNoMoyY0VH/KlAaTGkVXCIyAXAnwE/8Lgx5rdR5yV0/kLgIHCNMWZFvLEi0hl4HugFbAa+bYxJfbPmlDzZNzMKv9egUMl4nfbibaBuT7wtYcP1QkuIyEqboK6P5hGPWAJFaTgpFsppExwi4gemA+cBZcAyEZlrjFnruGws0C/0NRR4DBiaYOxPgf8YY34rIj8Nvb4zpZM/ioSGMbDOnMRFNffjex/M+//CGCsySkTwiXDc17K4eVS/CC0hFhPyc8OOcYC7XlnFnv1VHNe+TYSzHI7Y1XfsPRQuolddEwwLjO17D/FyqK94c998WzLJaEYlWyqY8c5nrN1RSVUgSE0gyOFqK8DBSdCY0N/RVMb53uMB/6P4wVPRTKUJCFTBKzdaP6dAeKRT4xgClBpjNgKIyHPAeMApOMYDTxpjDFAsIp1EpCuWNhFr7HhgZGj8bOBtUi04Sv6e0ts1Nvb/8Soy+UnN9cwNDgfA6bu2fraq25btPcxdr6wCiCk8orWGvG4duXfu6nCvB4AXS8p49vqiOu1JfXIk9yMI7D9Uw+UzPwiPdY5T0oMXzahkSwWT/vI+oWhqTwQNvBoczqu1w3ky8z5G+Nao8GiumCD8Z1pKBIf3pm/JcxKwzfG6LHTMyzXxxp5gjNkJEPp+vNubi8gNIrJcRJbv3r07uZmbNPgWGgFjjmgYvaueYUDV7LDQ8MIbq2Pbq6Pt5G+s3hmRNQ6RpdKd1wccG5FPYM3OfRFjneOaKwM6D2BA5wFNPY20UryxPCmhEc1VNT/nhzU3cyjoD/8tKs2MyrKU3CadGofbc0f0n1Ksa7yMjYsxZiYwE6CwsDC5P2HxtxjhYf/nNMA/AqO5p3ZKve81dmDXmOfcqqou2VgeoXE47eexyq+7jW0JDvI7h6RWqW2OWJV1aZDwmBscztxq62HljawfM4Dt4XOqiTQDOuam5DbpFBxlQHfH61xgh8drsuKM3SUiXY0xO0NmrS9SOmuAgmualY8j1pNbFZn8LHA988yIsM05wxcyQDn8GEfs0XXPefVxxKqqOmdFmauPw638utvY6ERCpeko6JnD8zf+T5I+jth/b9+svT987hcZs7jCt8D1iVAFSiMhPjh3ampuZdKkT4pIBvApcC6wHVgGTDbGrHFccxFwK1ZU1VDgIWPMkHhjReR+oNzhHO9sjPlJvLkUFhaa5cuXJ7eA5uQgb2A0lKI0W5rT/7OjmXpGVYlIiTGmsM7xdAmO0JteCDyIFVI7yxhzn4jcBGCMmREKx30EuAArHPdaY8zyWGNDx7sALwA9gK3AZcaYL+PNo16CQ1EUpZXTJIKjuaCCQ1EUJXliCY50RlUpiqIoRyEqOBRFUZSkUMGhKIqiJIUKDkVRFCUpWoVzXER2A1vqOfxYYE8Kp9MS0DW3DnTNrYOGrLmnMea46IOtQnA0BBFZ7hZVcDSja24d6JpbB+lYs5qqFEVRlKRQwaEoiqIkhQqOxMxs6gk0Abrm1oGuuXWQ8jWrj0NRFEVJCtU4FEVRlKRQwaEoiqIkhQqOECJygYisF5HSULn26PMiIg+Fzq8UkfymmGcq8bDmK0JrXSki74vIN5pinqkk0Zod150hIgERubQx55cOvKxZREaKyEciskZE3mnsOaYaD3/bHUXknyLycWjN1zbFPFOFiMwSkS9EZHWM86ndv4wxrf4Lq3T7Z0AfrCZSHwOnRl1zIfAGVnfCImBJU8+7Edb8P0BO6OexrWHNjuv+C7wOXNrU826E33MnYC3QI/T6+KaedyOs+S7gd6GfjwO+BLKaeu4NWPNZQD6wOsb5lO5fqnFYDAFKjTEbjTHVwHPA+KhrxgNPGotioFOoA2FLJeGajTHvG2MqQi+LsToxtmS8/J4B/heYQzq6SzY+XtY8GXjZGLMVwBjT0tftZc0GaB/qCfQ1LMFR27jTTB3GmHex1hCLlO5fKjgsTgK2OV6XhY4le01LItn1fA/riaUlk3DNInIScAkwoxHnlU68/J5PAXJE5G0RKRGRqxptdunBy5ofAb6O1ZJ6FfBDY0wDuq03e1K6f6Wz53hLwq3rcXScspdrWhKe1yMio7AEx/C0zij9eFnzg8CdxpiAHB3NsL2sOQMowGrV3Bb4QESKjTGfpntyacLLmscAHwHnACcDb4nIImPMvjTPralI6f6lgsOiDOjueJ2L9SSS7DUtCU/rEZHBwOPAWGNMeSPNLV14WXMh8FxIaBwLXCgitcaYVxtlhqnH69/2HmPMAeCAiLwLfANoqYLDy5qvBX5rLAdAqYhsAgYASxtnio1OSvcvNVVZLAP6iUhvEckCvgPMjbpmLnBVKDqhCKg0xuxs7ImmkIRrFpEewMvAd1vw06eThGs2xvQ2xvQyxvQCXgJubsFCA7z9bb8GjBCRDBFpBwwFPmnkeaYSL2veiqVhISInAP2BjY06y8YlpfuXahyAMaZWRG4F/o0VkTHLGLNGRG4KnZ+BFWFzIVAKHMR6YmmxeFzzVKAL8GjoCbzWtODKoh7XfFThZc3GmE9E5E1gJRAEHjfGuIZ1tgQ8/p5/CfxdRFZhmXHuNMa02HLrIvIsMBI4VkTKgHuATEjP/qUlRxRFUZSkUFOVoiiKkhQqOBRFUZSkUMGhKIqiJIUKDkVRFCUpVHAoiqIoSaGCQ2l1iMi9InJH6OdpIjI6zrXfEpFT45y/KV6JDhHpJSKTGzbj8L1Gisj/xDl/gYgsFZF1oUq3z4dycezqqP8nIhtE5FMRWSgieY6xm0VkVaha7HwROTEVc1aOTlRwKK0aY8xUY8yCOJd8C3AVHCKSEcqDeDLO+F5YRQRTwUisisVucxkIPAxcbYwZYIw5DXg69P4At4TGfsMYcwrwG2CuiGQ7bjPKGPMNYDlW9VhFcUUFh9IqEJGfh/ozLMDKEraP/11CPTdE5LcisjbUr+CB0NP9xcD9oSf4k0OFAH8tVs+KH0ZpL31FZEHoqX2FiJwM/BYrK/sjEflR1JxGisi7IvJK6H1niIgvdO6C0D0+FpH/iEgv4CbgR6F7jYha4p3Ar40x4YxvY8zcUNVU+/z/GmMOhs7NB94HrnD5uN4F+tbjY1ZaCZo5rhz1iEgBVtmJ07H+5lcAJVHXdMaqijvAGGNEpJMxZq+IzAXmGWNeCl0H0MkYc3bo9b2O2zyNVf/oldCTvA/4KXCHMWZcjOkNwdJotgBvAhNCQumvwFnGmE0i0tkY86WIzAC+MsY84HKfPMDtOCLSATjGGPNZ1KnloXHRjMOqGKsorqjGobQGRgCvGGMOhqqfRtctAtgHHAYeF5EJWGUZYvF89AERaQ+cZIx5BcAYc9h+uk/A0lDfiADwLFYF4iLgXWPMptC94vVZqIOIdAlpJZ/a2lCsS4mskLpQRD4COmCZshTFFRUcSmshbm0dY0wt1tP/HCy/xptxLj/gcqy+Ndij52Wou6F7YQ1WBziMMeUhH8dM4GshYXlARPpEjcnH6vxnM8oYc5ox5ipjzN4k319pRajgUFoD7wKXiEjbkGbwzegLRORrQEdjzOvAbcBpoVP7gfaJ3iC0OZeJyLdC92sTqjSbaPyQUBVXHzAJeA/4ADhbRHqH7tXZw1x+D/xcRL7uONbO8fP9wEMi0jZ0z9FY2s0zidamKNGo4FCOeowxK7DMSx9haRSLXC5rD8wTkZXAO4DtyH4O+LGIfBhydsfju8APQvd4HzgRq+JsbcjJ/SOXMR9gOdBXA5uwTGq7gRuAl0XkY46Yxv6JJQDrOMeNMauAHwJPhsJxF2N1uLMFw8NY5cZXich64G5gvDHmUII1KUodtDquojQRIjKS+I5zRWmWqMahKIqiJIVqHIqiKEpSqMahKIqiJIUKDkVRFCUpVHAoiqIoSaGCQ1EURUkKFRyKoihKUvx/VWfGZBZwuxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.407 3.13665\n",
      "0.33667 fractional expected GOP seats =  2.693  out of  8 seats\n",
      "simpler expected GOP seats =  2.8555  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "plt.show()\n",
    "\n",
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\", state=\"+str(STATE), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",r3(Rsimple) ,r5(Rsmeared) )   \n",
    "print(r5(smearedSeats),\"fractional expected GOP seats = \",r3(nDistricts*smearedSeats),\" out of \",nDistricts,\"seats\")\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96a159db-98f9-453b-90a6-2f2713e042bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f802d8-479a-4fe4-aaec-70363cfd4ec7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
